5118 lines
656 KiB
Plaintext
5118 lines
656 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"x1 = [1, 2, 2, 3, 4, 4, 4, 4, 4, 5, 5]\n",
|
||
"x2 = [1, 1, 1, 2, 2, 3, 3, 3, 3, 4, 5, 5, 5]\n",
|
||
"bins = [x + 0.5 for x in range(0, 6)]\n",
|
||
"plt.hist([x1, x2], bins = bins, color = ['yellow', 'green'],\n",
|
||
" edgecolor = 'red', hatch = '/', label = ['x1', 'x2'],\n",
|
||
" histtype = 'bar') # bar est le defaut\n",
|
||
"plt.ylabel('valeurs')\n",
|
||
"plt.xlabel('nombres')\n",
|
||
"plt.title('2 series')\n",
|
||
"plt.legend()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"df = pd.read_stata('/Users/waelbousselmi/Google Drive/docs_multiples_chocs/doc_Avril_2021/plm_MC_190712_v1.dta')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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|
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" <thead>\n",
|
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" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>var1</th>\n",
|
||
" <th>period</th>\n",
|
||
" <th>nmarket</th>\n",
|
||
" <th>direc_shock</th>\n",
|
||
" <th>post_shock1_n</th>\n",
|
||
" <th>post_shock2_n</th>\n",
|
||
" <th>uu</th>\n",
|
||
" <th>dd</th>\n",
|
||
" <th>t0</th>\n",
|
||
" <th>t_shock</th>\n",
|
||
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|
||
" <th>r6_neg</th>\n",
|
||
" <th>r11_neg</th>\n",
|
||
" <th>r6_shock</th>\n",
|
||
" <th>r11_shock</th>\n",
|
||
" <th>std_risk</th>\n",
|
||
" <th>mean_risk_v1</th>\n",
|
||
" <th>median_risk_v1</th>\n",
|
||
" <th>numvar</th>\n",
|
||
" <th>numvarup</th>\n",
|
||
" <th>numvardown</th>\n",
|
||
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|
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|
||
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"</table>\n",
|
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|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" var1 period nmarket direc_shock post_shock1_n post_shock2_n \\\n",
|
||
"0 170703_0850 1 1 0 0.0 NaN \n",
|
||
"1 170703_0850 2 1 0 0.0 NaN \n",
|
||
"2 170703_0850 3 1 0 0.0 NaN \n",
|
||
"3 170703_0850 4 1 0 0.0 NaN \n",
|
||
"4 170703_0850 5 1 0 0.0 NaN \n",
|
||
"\n",
|
||
" uu dd t0 t_shock ... r6_neg r11_neg r6_shock r11_shock std_risk \\\n",
|
||
"0 1 0 0 1 ... 0.0 0.0 0.0 0.0 2.864 \n",
|
||
"1 1 0 0 1 ... 0.0 0.0 0.0 0.0 3.076 \n",
|
||
"2 1 0 0 1 ... 0.0 0.0 0.0 0.0 3.170 \n",
|
||
"3 1 0 0 1 ... 0.0 0.0 0.0 0.0 3.595 \n",
|
||
"4 1 0 0 1 ... 0.0 0.0 0.0 0.0 3.753 \n",
|
||
"\n",
|
||
" mean_risk_v1 median_risk_v1 numvar numvarup numvardown \n",
|
||
"0 4.088 3.75 NaN NaN NaN \n",
|
||
"1 4.938 5.00 2.0 0.0 2.0 \n",
|
||
"2 4.475 3.75 1.0 1.0 0.0 \n",
|
||
"3 5.688 6.00 2.0 0.0 2.0 \n",
|
||
"4 5.075 3.75 1.0 1.0 0.0 \n",
|
||
"\n",
|
||
"[5 rows x 65 columns]"
|
||
]
|
||
},
|
||
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|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df.head()"
|
||
]
|
||
},
|
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{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>ptft</th>\n",
|
||
" <th>rad_n</th>\n",
|
||
" <th>rd_n</th>\n",
|
||
" <th>t0</th>\n",
|
||
" <th>uu</th>\n",
|
||
" <th>dd</th>\n",
|
||
" <th>period</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
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|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>-10.00</td>\n",
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||
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|
||
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|
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|
||
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|
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"</table>\n",
|
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"</div>"
|
||
],
|
||
"text/plain": [
|
||
" ptft rad_n rd_n t0 uu dd period\n",
|
||
"0 -10.00 3.333333 -3.333333 0 1 0 1\n",
|
||
"1 0.00 1.666667 -1.666667 0 1 0 2\n",
|
||
"2 5.00 1.666667 -0.555556 0 1 0 3\n",
|
||
"3 10.00 2.083333 0.416667 0 1 0 4\n",
|
||
"4 5.05 2.003333 0.670000 0 1 0 5"
|
||
]
|
||
},
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"ptft = df[['ptft','rad_n', 'rd_n','t0','uu','dd','period']]\n",
|
||
"ptft.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"ptft_p1 = ptft[ptft['period']<6]\n",
|
||
"ptft_p2 = ptft[(ptft.period >5) & (ptft.period < 11) ]\n",
|
||
"ptft_p3 = ptft[(ptft.period >10)]\n",
|
||
"ptft_p1_t0 = ptft_p1[ptft_p1.t0 == 1]\n",
|
||
"ptft_p1_uu = ptft_p1[ptft_p1.uu == 1]\n",
|
||
"ptft_p1_dd = ptft_p1[ptft_p1.dd == 1]\n",
|
||
"ptft_p2_t0 = ptft_p2[ptft_p2.t0 == 1]\n",
|
||
"ptft_p2_uu = ptft_p2[ptft_p2.uu == 1]\n",
|
||
"ptft_p2_dd = ptft_p2[ptft_p2.dd == 1]\n",
|
||
"ptft_p3_t0 = ptft_p3[ptft_p3.t0 == 1]\n",
|
||
"ptft_p3_uu = ptft_p3[ptft_p3.uu == 1]\n",
|
||
"ptft_p3_dd = ptft_p3[ptft_p3.dd == 1]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
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{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import statistics as st\n",
|
||
"v11 = st.mean(ptft_p1_t0.ptft)\n",
|
||
"v12 = st.mean(ptft_p2_t0.ptft)\n",
|
||
"v13 = st.mean(ptft_p3_t0.ptft)\n",
|
||
"v21 = st.mean(ptft_p1_uu.ptft)\n",
|
||
"v22 = st.mean(ptft_p2_uu.ptft)\n",
|
||
"v23 = st.mean(ptft_p3_uu.ptft)\n",
|
||
"v31 = st.mean(ptft_p1_dd.ptft)\n",
|
||
"v32 = st.mean(ptft_p2_dd.ptft)\n",
|
||
"v33 = st.mean(ptft_p3_dd.ptft)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<BarContainer object of 3 artists>"
|
||
]
|
||
},
|
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"execution_count": 7,
|
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"metadata": {},
|
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"output_type": "execute_result"
|
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},
|
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{
|
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"data": {
|
||
"image/png": "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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"data = [[v11, v21, v31],\n",
|
||
"[v12, v22, v32],\n",
|
||
"[v13, v23, v33]]\n",
|
||
"X = np.arange(3)\n",
|
||
"fig = plt.figure()\n",
|
||
"ax = fig.add_axes([0,0,1,1])\n",
|
||
"ax.bar(X + 0.00, data[0], color = 'b', width = 0.25)\n",
|
||
"ax.bar(X + 0.25, data[1], color = 'g', width = 0.25)\n",
|
||
"ax.bar(X + 0.50, data[2], color = 'r', width = 0.25)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<BarContainer object of 3 artists>"
|
||
]
|
||
},
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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VZ64AXVVPypl18x/nfdLDaV3Xzb6s89rZ/r3flORUd79hl83Wcv3s59is0tpZi6d2e5fLGVbVS7bv/9OcuULTs5N8Lsm3krzooOY9n/Z5bJ6X5KVVdUeS/05yWW+/rO6erqquyplXDx6tqtNJrkhyr2S918137OP4rO3aSfLUJC9I8omqunH7tt9KcnGy9utnP8dmZdaOKxsBwMC6PLULAOeEkALAgJACwICQAsCAkALAgJACwICQAsCAkALAwP8BZraKVtDdHioAAAAASUVORK5CYII=\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import statistics as st\n",
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"v11 = st.mean(ptft_p1_t0.rad_n)\n",
|
||
"v12 = st.mean(ptft_p2_t0.rad_n)\n",
|
||
"v13 = st.mean(ptft_p3_t0.rad_n)\n",
|
||
"v21 = st.mean(ptft_p1_uu.rad_n)\n",
|
||
"v22 = st.mean(ptft_p2_uu.rad_n)\n",
|
||
"v23 = st.mean(ptft_p3_uu.rad_n)\n",
|
||
"v31 = st.mean(ptft_p1_dd.rad_n)\n",
|
||
"v32 = st.mean(ptft_p2_dd.rad_n)\n",
|
||
"v33 = st.mean(ptft_p3_dd.rad_n)\n",
|
||
"data = [[v11, v21, v31],\n",
|
||
"[v12, v22, v32],\n",
|
||
"[v13, v23, v33]]\n",
|
||
"X = np.arange(3)\n",
|
||
"fig = plt.figure()\n",
|
||
"ax = fig.add_axes([0,0,1,1])\n",
|
||
"ax.bar(X + 0.00, data[0], color = 'b', width = 0.25)\n",
|
||
"ax.bar(X + 0.25, data[1], color = 'g', width = 0.25)\n",
|
||
"ax.bar(X + 0.50, data[2], color = 'r', width = 0.25)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<BarContainer object of 3 artists>"
|
||
]
|
||
},
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import statistics as st\n",
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"v11 = st.median(ptft_p1_t0.rad_n)\n",
|
||
"v12 = st.median(ptft_p2_t0.rad_n)\n",
|
||
"v13 = st.median(ptft_p3_t0.rad_n)\n",
|
||
"v21 = st.median(ptft_p1_uu.rad_n)\n",
|
||
"v22 = st.median(ptft_p2_uu.rad_n)\n",
|
||
"v23 = st.median(ptft_p3_uu.rad_n)\n",
|
||
"v31 = st.median(ptft_p1_dd.rad_n)\n",
|
||
"v32 = st.median(ptft_p2_dd.rad_n)\n",
|
||
"v33 = st.median(ptft_p3_dd.rad_n)\n",
|
||
"data = [[v11, v21, v31],\n",
|
||
"[v12, v22, v32],\n",
|
||
"[v13, v23, v33]]\n",
|
||
"X = np.arange(3)\n",
|
||
"fig = plt.figure()\n",
|
||
"ax = fig.add_axes([0,0,1,1])\n",
|
||
"ax.bar(X + 0.00, data[0], color = 'b', width = 0.25)\n",
|
||
"ax.bar(X + 0.25, data[1], color = 'g', width = 0.25)\n",
|
||
"ax.bar(X + 0.50, data[2], color = 'r', width = 0.25)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<BarContainer object of 3 artists>"
|
||
]
|
||
},
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import statistics as st\n",
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"v11 = st.mean(ptft_p1_t0.rd_n)\n",
|
||
"v12 = st.mean(ptft_p2_t0.rd_n)\n",
|
||
"v13 = st.mean(ptft_p3_t0.rd_n)\n",
|
||
"v21 = st.mean(ptft_p1_uu.rd_n)\n",
|
||
"v22 = st.mean(ptft_p2_uu.rd_n)\n",
|
||
"v23 = st.mean(ptft_p3_uu.rd_n)\n",
|
||
"v31 = st.mean(ptft_p1_dd.rd_n)\n",
|
||
"v32 = st.mean(ptft_p2_dd.rd_n)\n",
|
||
"v33 = st.mean(ptft_p3_dd.rd_n)\n",
|
||
"data = [[v11, v21, v31],\n",
|
||
"[v12, v22, v32],\n",
|
||
"[v13, v23, v33]]\n",
|
||
"X = np.arange(3)\n",
|
||
"fig = plt.figure()\n",
|
||
"ax = fig.add_axes([0,0,1,1])\n",
|
||
"ax.bar(X + 0.00, data[0], color = 'b', width = 0.25)\n",
|
||
"ax.bar(X + 0.25, data[1], color = 'g', width = 0.25)\n",
|
||
"ax.bar(X + 0.50, data[2], color = 'r', width = 0.25)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<BarContainer object of 3 artists>"
|
||
]
|
||
},
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": "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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import statistics as st\n",
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"v11 = st.median(ptft_p1_t0.rd_n)\n",
|
||
"v12 = st.median(ptft_p2_t0.rd_n)\n",
|
||
"v13 = st.median(ptft_p3_t0.rd_n)\n",
|
||
"v21 = st.median(ptft_p1_uu.rd_n)\n",
|
||
"v22 = st.median(ptft_p2_uu.rd_n)\n",
|
||
"v23 = st.median(ptft_p3_uu.rd_n)\n",
|
||
"v31 = st.median(ptft_p1_dd.rd_n)\n",
|
||
"v32 = st.median(ptft_p2_dd.rd_n)\n",
|
||
"v33 = st.median(ptft_p3_dd.rd_n)\n",
|
||
"data = [[v11, v21, v31],\n",
|
||
"[v12, v22, v32],\n",
|
||
"[v13, v23, v33]]\n",
|
||
"X = np.arange(3)\n",
|
||
"fig = plt.figure()\n",
|
||
"ax = fig.add_axes([0,0,1,1])\n",
|
||
"ax.bar(X + 0.00, data[0], color = 'b', width = 0.25)\n",
|
||
"ax.bar(X + 0.25, data[1], color = 'g', width = 0.25)\n",
|
||
"ax.bar(X + 0.50, data[2], color = 'r', width = 0.25)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 113,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div style=\"float: left; padding: 10px;\">\n",
|
||
" <p style='font-family:\"Courier New\", Courier, monospace'>df1</p><div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>employee</th>\n",
|
||
" <th>group</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Bob</td>\n",
|
||
" <td>Accounting</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Jake</td>\n",
|
||
" <td>Engineering</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Lisa</td>\n",
|
||
" <td>Engineering</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Sue</td>\n",
|
||
" <td>HR</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>\n",
|
||
" </div>\n",
|
||
"<div style=\"float: left; padding: 10px;\">\n",
|
||
" <p style='font-family:\"Courier New\", Courier, monospace'>df2</p><div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>employee</th>\n",
|
||
" <th>hire_date</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Lisa</td>\n",
|
||
" <td>2004</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Bob</td>\n",
|
||
" <td>2008</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Jake</td>\n",
|
||
" <td>2012</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Sue</td>\n",
|
||
" <td>2014</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>\n",
|
||
" </div>"
|
||
],
|
||
"text/plain": [
|
||
"df1\n",
|
||
" employee group\n",
|
||
"0 Bob Accounting\n",
|
||
"1 Jake Engineering\n",
|
||
"2 Lisa Engineering\n",
|
||
"3 Sue HR\n",
|
||
"\n",
|
||
"df2\n",
|
||
" employee hire_date\n",
|
||
"0 Lisa 2004\n",
|
||
"1 Bob 2008\n",
|
||
"2 Jake 2012\n",
|
||
"3 Sue 2014"
|
||
]
|
||
},
|
||
"execution_count": 113,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"class display(object):\n",
|
||
" \"\"\"Display HTML representation of multiple objects\"\"\"\n",
|
||
" template = \"\"\"<div style=\"float: left; padding: 10px;\">\n",
|
||
" <p style='font-family:\"Courier New\", Courier, monospace'>{0}</p>{1}\n",
|
||
" </div>\"\"\"\n",
|
||
" def __init__(self, *args):\n",
|
||
" self.args = args\n",
|
||
" \n",
|
||
" def _repr_html_(self):\n",
|
||
" return '\\n'.join(self.template.format(a, eval(a)._repr_html_())\n",
|
||
" for a in self.args)\n",
|
||
" \n",
|
||
" def __repr__(self):\n",
|
||
" return '\\n\\n'.join(a + '\\n' + repr(eval(a))\n",
|
||
" for a in self.args)\n",
|
||
"df1 = pd.DataFrame({'employee': ['Bob', 'Jake', 'Lisa', 'Sue'],\n",
|
||
" 'group': ['Accounting', 'Engineering', 'Engineering', 'HR']})\n",
|
||
"df2 = pd.DataFrame({'employee': ['Lisa', 'Bob', 'Jake', 'Sue'],\n",
|
||
" 'hire_date': [2004, 2008, 2012, 2014]})\n",
|
||
"display('df1', 'df2') "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {
|
||
"collapsed": true,
|
||
"jupyter": {
|
||
"outputs_hidden": true
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>employee</th>\n",
|
||
" <th>group</th>\n",
|
||
" <th>hire_date</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Bob</td>\n",
|
||
" <td>Accounting</td>\n",
|
||
" <td>2008</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Jake</td>\n",
|
||
" <td>Engineering</td>\n",
|
||
" <td>2012</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Lisa</td>\n",
|
||
" <td>Engineering</td>\n",
|
||
" <td>2004</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Sue</td>\n",
|
||
" <td>HR</td>\n",
|
||
" <td>2014</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" employee group hire_date\n",
|
||
"0 Bob Accounting 2008\n",
|
||
"1 Jake Engineering 2012\n",
|
||
"2 Lisa Engineering 2004\n",
|
||
"3 Sue HR 2014"
|
||
]
|
||
},
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df3 = pd.merge(df1, df2)\n",
|
||
"#df3"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 127,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df4 = pd.concat([df1, df1])\n",
|
||
"#df4"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 128,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>employee</th>\n",
|
||
" <th>group</th>\n",
|
||
" <th>test</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Bob</td>\n",
|
||
" <td>Accounting</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Jake</td>\n",
|
||
" <td>Engineering</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Lisa</td>\n",
|
||
" <td>Engineering</td>\n",
|
||
" <td>3</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Sue</td>\n",
|
||
" <td>HR</td>\n",
|
||
" <td>3</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Bob</td>\n",
|
||
" <td>Accounting</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Jake</td>\n",
|
||
" <td>Engineering</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Lisa</td>\n",
|
||
" <td>Engineering</td>\n",
|
||
" <td>3</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Sue</td>\n",
|
||
" <td>HR</td>\n",
|
||
" <td>3</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" employee group test\n",
|
||
"0 Bob Accounting 1\n",
|
||
"1 Jake Engineering 1\n",
|
||
"2 Lisa Engineering 3\n",
|
||
"3 Sue HR 3\n",
|
||
"0 Bob Accounting 1\n",
|
||
"1 Jake Engineering 1\n",
|
||
"2 Lisa Engineering 3\n",
|
||
"3 Sue HR 3"
|
||
]
|
||
},
|
||
"execution_count": 128,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df4"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 151,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>employee</th>\n",
|
||
" <th>group</th>\n",
|
||
" <th>Address</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Bob</td>\n",
|
||
" <td>Accounting</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Jake</td>\n",
|
||
" <td>Engineering</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Lisa</td>\n",
|
||
" <td>Engineering</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Sue</td>\n",
|
||
" <td>HR</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" employee group Address\n",
|
||
"0 Bob Accounting 0\n",
|
||
"1 Jake Engineering 0\n",
|
||
"2 Lisa Engineering 0\n",
|
||
"3 Sue HR 0"
|
||
]
|
||
},
|
||
"execution_count": 151,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"df3 = df1.copy()\n",
|
||
"df3['Address']=0\n",
|
||
"df3 "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 147,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"1\n",
|
||
"2\n",
|
||
"3\n",
|
||
"4\n",
|
||
" employee group Address\n",
|
||
"0 Bob Accounting 1\n",
|
||
"1 Jake Engineering 6\n",
|
||
"2 Lisa Engineering 11\n",
|
||
"3 Sue HR 16\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"b=0\n",
|
||
"a=1\n",
|
||
"for i in range(0,4):\n",
|
||
" df3.at[b,'Address']=a\n",
|
||
" a+=5\n",
|
||
" b+=1\n",
|
||
" print(b) \n",
|
||
"print(df3) "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 148,
|
||
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|
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||
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||
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|
||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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|
||
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||
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|
||
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||
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||
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|
||
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||
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||
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||
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||
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||
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||
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||
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|
||
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||
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|
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|
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
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|
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"text/plain": [
|
||
" employee group Address\n",
|
||
"0 Bob Accounting 1\n",
|
||
"1 Jake Engineering 6\n",
|
||
"2 Lisa Engineering 11\n",
|
||
"3 Sue HR 16\n",
|
||
"0 Bob Accounting 2\n",
|
||
"1 Jake Engineering 7\n",
|
||
"2 Lisa Engineering 12\n",
|
||
"3 Sue HR 17\n",
|
||
"0 Bob Accounting 3\n",
|
||
"1 Jake Engineering 8\n",
|
||
"2 Lisa Engineering 13\n",
|
||
"3 Sue HR 18"
|
||
]
|
||
},
|
||
"execution_count": 152,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"df3 = df1.copy()\n",
|
||
"df3['Address']=0\n",
|
||
"#\n",
|
||
"b=0\n",
|
||
"a=1\n",
|
||
"for i in range(0,4):\n",
|
||
" df3.at[b,'Address']=a\n",
|
||
" a+=5\n",
|
||
" b+=1 \n",
|
||
"# \n",
|
||
"df4=df3.copy()\n",
|
||
"b=0\n",
|
||
"a=2\n",
|
||
"c=2\n",
|
||
"for i in range(1,3):\n",
|
||
" for i in range(0,4):\n",
|
||
" df3.at[b,'Address']=a\n",
|
||
" a+=5\n",
|
||
" b+=1 \n",
|
||
" df4 = pd.concat([df4, df3])\n",
|
||
" b=0\n",
|
||
" c+=1\n",
|
||
" a=c\n",
|
||
" print(\"A\")\n",
|
||
" print(df4)\n",
|
||
"df4"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {
|
||
"collapsed": true,
|
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"jupyter": {
|
||
"outputs_hidden": true
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"ename": "NameError",
|
||
"evalue": "name 'df1' is not defined",
|
||
"output_type": "error",
|
||
"traceback": [
|
||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
||
"\u001b[0;32m<ipython-input-18-640c0bc44963>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mdf3\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'count'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# création de la colonne count\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||
"\u001b[0;31mNameError\u001b[0m: name 'df1' is not defined"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"df3 = df1.copy()\n",
|
||
"df3['count']=0\n",
|
||
"# création de la colonne count\n",
|
||
"b=0\n",
|
||
"a=0 #1\n",
|
||
"n=4 # 4 lignes // 2160 lignes \n",
|
||
"for i in range(0,n):\n",
|
||
" df3.at[b,'count']=a\n",
|
||
" a+= 5 #+=240\n",
|
||
" b+=1 \n",
|
||
"# on va répliquer la même chose 240 fois pour avoir une observation \n",
|
||
"# par seconde par sujet \n",
|
||
"df3['secnd']=0 #@\n",
|
||
"df4=df3.copy()\n",
|
||
"b=0\n",
|
||
"a=1 # 2\n",
|
||
"c=1 #2\n",
|
||
"n=4 # 4: répéter 5 fois // 240 / nbr de secondes 2min\n",
|
||
"v=4 # 2160 / (8 sujets * 18 marchés * 15 périodes)\n",
|
||
"for i in range(0,n):\n",
|
||
" for i in range(0,v):\n",
|
||
" df3.at[b,'count']=a\n",
|
||
" a+= 5 # +=120\n",
|
||
" b+=1\n",
|
||
" df3['secnd']=c #@ \n",
|
||
" df4 = pd.concat([df4, df3])\n",
|
||
" b=0\n",
|
||
" c+=1\n",
|
||
" a=c\n",
|
||
"# print(\"A\")\n",
|
||
"# print(df4)\n",
|
||
"df4\n",
|
||
"df5 = df4.sort_values(by=['count'])\n",
|
||
"df5"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# pour importer une table Excel\n",
|
||
"import pandas as pd\n",
|
||
"ddf = pd.read_excel('/Users/waelbousselmi/Desktop/data_mc_2021.xlsx')\n",
|
||
"#print(ddf)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"170703_0850 2160\n",
|
||
"nmarket 2160\n",
|
||
"traitement 2160\n",
|
||
"treament 2160\n",
|
||
"Period 2160\n",
|
||
"Subject 2160\n",
|
||
"UpperBound1 2160\n",
|
||
"MedianP1 2160\n",
|
||
"LowerBound1 2160\n",
|
||
"StartWealth 2160\n",
|
||
"EndWealth 2160\n",
|
||
"variation_wealth 2160\n",
|
||
"GainPrevision 2160\n",
|
||
"GainPrevisionCumule 2160\n",
|
||
"Risk_debutperiode 2160\n",
|
||
"Risk_finperiode 2160\n",
|
||
"variation_risk 2160\n",
|
||
"StartMoney 2160\n",
|
||
"StartStock 2160\n",
|
||
"EndMoney 2160\n",
|
||
"EndStock 2160\n",
|
||
"VarMoney 2160\n",
|
||
"VarStock 2160\n",
|
||
"TResult 2160\n",
|
||
"TotalDividende 2160\n",
|
||
"DividendeCumule 2160\n",
|
||
"MaxPrice 2160\n",
|
||
"MinPrice 2160\n",
|
||
"MeanPrice 2160\n",
|
||
"MedianPrice 2160\n",
|
||
"GainFinal 2160\n",
|
||
"GainFinalEUR 2160\n",
|
||
"GainForecastECU 2160\n",
|
||
"GainForecastEUR 2160\n",
|
||
"CompteEpargne 2160\n",
|
||
"CompteEpargneEUR 2160\n",
|
||
"dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"ddf.count()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
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|
||
"<style scoped>\n",
|
||
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|
||
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|
||
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|
||
"\n",
|
||
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|
||
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|
||
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|
||
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <th>Subject</th>\n",
|
||
" <th>UpperBound1</th>\n",
|
||
" <th>MedianP1</th>\n",
|
||
" <th>LowerBound1</th>\n",
|
||
" <th>StartWealth</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>MedianPrice</th>\n",
|
||
" <th>GainFinal</th>\n",
|
||
" <th>GainFinalEUR</th>\n",
|
||
" <th>GainForecastECU</th>\n",
|
||
" <th>GainForecastEUR</th>\n",
|
||
" <th>CompteEpargne</th>\n",
|
||
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|
||
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|
||
" <th>secnd</th>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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||
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||
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||
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||
" <td>3.00</td>\n",
|
||
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||
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||
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||
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||
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||
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|
||
" <td>2</td>\n",
|
||
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|
||
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|
||
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|
||
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|
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||
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||
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|
||
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||
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||
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||
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|
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||
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||
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||
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||
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||
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||
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
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|
||
" <tr>\n",
|
||
" <th>2159</th>\n",
|
||
" <td>171026_0838</td>\n",
|
||
" <td>18</td>\n",
|
||
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|
||
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|
||
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|
||
" <td>8</td>\n",
|
||
" <td>-111.0</td>\n",
|
||
" <td>-111.0</td>\n",
|
||
" <td>-111.0</td>\n",
|
||
" <td>7795.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>300.75</td>\n",
|
||
" <td>7810.0</td>\n",
|
||
" <td>23.14</td>\n",
|
||
" <td>2413.44</td>\n",
|
||
" <td>7.15</td>\n",
|
||
" <td>2128.44</td>\n",
|
||
" <td>6.31</td>\n",
|
||
" <td>261355</td>\n",
|
||
" <td>116</td>\n",
|
||
" <td>1796</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2159</th>\n",
|
||
" <td>171026_0838</td>\n",
|
||
" <td>18</td>\n",
|
||
" <td>NO</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>15</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>-111.0</td>\n",
|
||
" <td>-111.0</td>\n",
|
||
" <td>-111.0</td>\n",
|
||
" <td>7795.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>300.75</td>\n",
|
||
" <td>7810.0</td>\n",
|
||
" <td>23.14</td>\n",
|
||
" <td>2413.44</td>\n",
|
||
" <td>7.15</td>\n",
|
||
" <td>2128.44</td>\n",
|
||
" <td>6.31</td>\n",
|
||
" <td>261356</td>\n",
|
||
" <td>117</td>\n",
|
||
" <td>1797</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2159</th>\n",
|
||
" <td>171026_0838</td>\n",
|
||
" <td>18</td>\n",
|
||
" <td>NO</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>15</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>-111.0</td>\n",
|
||
" <td>-111.0</td>\n",
|
||
" <td>-111.0</td>\n",
|
||
" <td>7795.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>300.75</td>\n",
|
||
" <td>7810.0</td>\n",
|
||
" <td>23.14</td>\n",
|
||
" <td>2413.44</td>\n",
|
||
" <td>7.15</td>\n",
|
||
" <td>2128.44</td>\n",
|
||
" <td>6.31</td>\n",
|
||
" <td>261357</td>\n",
|
||
" <td>118</td>\n",
|
||
" <td>1798</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2159</th>\n",
|
||
" <td>171026_0838</td>\n",
|
||
" <td>18</td>\n",
|
||
" <td>NO</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>15</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>-111.0</td>\n",
|
||
" <td>-111.0</td>\n",
|
||
" <td>-111.0</td>\n",
|
||
" <td>7795.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>300.75</td>\n",
|
||
" <td>7810.0</td>\n",
|
||
" <td>23.14</td>\n",
|
||
" <td>2413.44</td>\n",
|
||
" <td>7.15</td>\n",
|
||
" <td>2128.44</td>\n",
|
||
" <td>6.31</td>\n",
|
||
" <td>261358</td>\n",
|
||
" <td>119</td>\n",
|
||
" <td>1799</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2159</th>\n",
|
||
" <td>171026_0838</td>\n",
|
||
" <td>18</td>\n",
|
||
" <td>NO</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>15</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>-111.0</td>\n",
|
||
" <td>-111.0</td>\n",
|
||
" <td>-111.0</td>\n",
|
||
" <td>7795.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>300.75</td>\n",
|
||
" <td>7810.0</td>\n",
|
||
" <td>23.14</td>\n",
|
||
" <td>2413.44</td>\n",
|
||
" <td>7.15</td>\n",
|
||
" <td>2128.44</td>\n",
|
||
" <td>6.31</td>\n",
|
||
" <td>261359</td>\n",
|
||
" <td>120</td>\n",
|
||
" <td>1800</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>261360 rows × 39 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" 170703_0850 nmarket traitement treament Period Subject UpperBound1 \\\n",
|
||
"0 170703_0850 1 HH 0 1 1 320.0 \n",
|
||
"0 170703_0850 1 HH 0 1 1 320.0 \n",
|
||
"0 170703_0850 1 HH 0 1 1 320.0 \n",
|
||
"0 170703_0850 1 HH 0 1 1 320.0 \n",
|
||
"0 170703_0850 1 HH 0 1 1 320.0 \n",
|
||
"... ... ... ... ... ... ... ... \n",
|
||
"2159 171026_0838 18 NO 2 15 8 -111.0 \n",
|
||
"2159 171026_0838 18 NO 2 15 8 -111.0 \n",
|
||
"2159 171026_0838 18 NO 2 15 8 -111.0 \n",
|
||
"2159 171026_0838 18 NO 2 15 8 -111.0 \n",
|
||
"2159 171026_0838 18 NO 2 15 8 -111.0 \n",
|
||
"\n",
|
||
" MedianP1 LowerBound1 StartWealth ... MedianPrice GainFinal \\\n",
|
||
"0 320.0 320.0 6750.0 ... 290.00 7350.0 \n",
|
||
"0 320.0 320.0 6750.0 ... 290.00 7350.0 \n",
|
||
"0 320.0 320.0 6750.0 ... 290.00 7350.0 \n",
|
||
"0 320.0 320.0 6750.0 ... 290.00 7350.0 \n",
|
||
"0 320.0 320.0 6750.0 ... 290.00 7350.0 \n",
|
||
"... ... ... ... ... ... ... \n",
|
||
"2159 -111.0 -111.0 7795.0 ... 300.75 7810.0 \n",
|
||
"2159 -111.0 -111.0 7795.0 ... 300.75 7810.0 \n",
|
||
"2159 -111.0 -111.0 7795.0 ... 300.75 7810.0 \n",
|
||
"2159 -111.0 -111.0 7795.0 ... 300.75 7810.0 \n",
|
||
"2159 -111.0 -111.0 7795.0 ... 300.75 7810.0 \n",
|
||
"\n",
|
||
" GainFinalEUR GainForecastECU GainForecastEUR CompteEpargne \\\n",
|
||
"0 21.78 1012.50 3.00 877.50 \n",
|
||
"0 21.78 1012.50 3.00 877.50 \n",
|
||
"0 21.78 1012.50 3.00 877.50 \n",
|
||
"0 21.78 1012.50 3.00 877.50 \n",
|
||
"0 21.78 1012.50 3.00 877.50 \n",
|
||
"... ... ... ... ... \n",
|
||
"2159 23.14 2413.44 7.15 2128.44 \n",
|
||
"2159 23.14 2413.44 7.15 2128.44 \n",
|
||
"2159 23.14 2413.44 7.15 2128.44 \n",
|
||
"2159 23.14 2413.44 7.15 2128.44 \n",
|
||
"2159 23.14 2413.44 7.15 2128.44 \n",
|
||
"\n",
|
||
" CompteEpargneEUR count secnd secnd2 \n",
|
||
"0 2.60 0 0 0 \n",
|
||
"0 2.60 1 1 1 \n",
|
||
"0 2.60 2 2 2 \n",
|
||
"0 2.60 3 3 3 \n",
|
||
"0 2.60 4 4 4 \n",
|
||
"... ... ... ... ... \n",
|
||
"2159 6.31 261355 116 1796 \n",
|
||
"2159 6.31 261356 117 1797 \n",
|
||
"2159 6.31 261357 118 1798 \n",
|
||
"2159 6.31 261358 119 1799 \n",
|
||
"2159 6.31 261359 120 1800 \n",
|
||
"\n",
|
||
"[261360 rows x 39 columns]"
|
||
]
|
||
},
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"#1ere étape (note garder l'ordre)\n",
|
||
"import numpy as np\n",
|
||
"ddf3 = ddf.copy()\n",
|
||
"ddf3['count']=0\n",
|
||
"# création de la colonne count\n",
|
||
"b=0\n",
|
||
"a=0\n",
|
||
"n=2160 # 4 lignes // 2160 lignes \n",
|
||
"for i in range(0,n):\n",
|
||
" ddf3.at[b,'count']=a\n",
|
||
" a+= 121 # +=121\n",
|
||
" b+=1\n",
|
||
"# 2eme étape \n",
|
||
"# on va répliquer la même chose 120 fois pour avoir une observation \n",
|
||
"# par seconde par sujet \n",
|
||
"ddf3['secnd']=0 #@\n",
|
||
"ddf4=ddf3.copy()\n",
|
||
"b=0\n",
|
||
"a=1\n",
|
||
"c=1\n",
|
||
"n=120 # 4: répéter 5 fois // 120 nbr de secondes 2min (avec sec=0 avant de commencer le marché)\n",
|
||
"v=2160 # 2160 / (8 sujets * 18 marchés * 15 périodes)\n",
|
||
"for i in range(0,n):\n",
|
||
" for i in range(0,v):\n",
|
||
" ddf3.at[b,'count']=a\n",
|
||
" a+=121 # +=121 2min\n",
|
||
" b+=1\n",
|
||
" ddf3['secnd']=c #@ \n",
|
||
" ddf4 = pd.concat([ddf4, ddf3])\n",
|
||
" b=0\n",
|
||
" c+=1\n",
|
||
" a=c\n",
|
||
"#ddf4\n",
|
||
"ddf4['secnd2']= ddf4['secnd'] + (120* (ddf4['Period']-1))\n",
|
||
"ddf5 = ddf4.sort_values(by=['count'])\n",
|
||
"ddf5"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 322,
|
||
"metadata": {
|
||
"collapsed": true,
|
||
"jupyter": {
|
||
"outputs_hidden": true
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"ename": "NameError",
|
||
"evalue": "name 'ddf5' is not defined",
|
||
"output_type": "error",
|
||
"traceback": [
|
||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
||
"\u001b[0;32m<ipython-input-322-a810a7373b76>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mddf5\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'nmarket'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Subject'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Period'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'secnd'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mddf5\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr'/Users/waelbousselmi/Desktop/ddf5.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||
"\u001b[0;31mNameError\u001b[0m: name 'ddf5' is not defined"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"ddf5.to_csv(r'/Users/waelbousselmi/Desktop/ddf5.csv', index = False, header=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# pour importer une table Excel\n",
|
||
"import pandas as pd\n",
|
||
"df6 = pd.read_excel('/Users/waelbousselmi/Desktop/risque_continu.xlsx')\n",
|
||
"#print(ddf)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"df7 = pd.read_csv('/Users/waelbousselmi/Desktop/ddf5.csv')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 71,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 72,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 315,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"875"
|
||
]
|
||
},
|
||
"execution_count": 315,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"#df6.shape[0] #875\n",
|
||
"#df7.shape[0] # 261360"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"alpha = 34 sur 34\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"###### pour intégrer les valeurs risque \n",
|
||
"df7['risk_test']= \"\"\n",
|
||
"a = df7.shape[0] # 875\n",
|
||
"b = 34 # df6.shape[0] # 261360\n",
|
||
"alpha = 0\n",
|
||
"c=0\n",
|
||
"for i in range (0,b):\n",
|
||
" for i in range (0,a):\n",
|
||
" if (df6.at[c,\"nmarket\"]==df7.at[i,\"nmarket\"] and \n",
|
||
" df6.at[c,\"Subject\"]==df7.at[i,\"Subject\"] and\n",
|
||
" df6.at[c,\"Period\"]==df7.at[i,\"Period\"] and \n",
|
||
" df6.at[c,\"treatment\"]==df7.at[i,\"traitement\"] and \n",
|
||
" df6.at[c,\"timerisk_start\"]==df7.at[i,\"secnd2\"]):\n",
|
||
"# print(\"A\",i, \"/ sujet=\",df6.at[c,\"Subject\"], \"/ période=\",df6.at[c,\"Period\"],\"/ traitement\",df6.at[c,\"treatment\"], \"/ nmarket\",df6.at[c,\"nmarket\"],\n",
|
||
"# \"/ q1_risk=\", df6.at[c,\"q1_risk\"], \"/ temps_sec:\",df6.at[c,\"timerisk_start\"] )\n",
|
||
" df7.at[i,'risk_test'] = df6.at[c,\"q1_risk\"]\n",
|
||
" alpha +=1 \n",
|
||
" c+=1\n",
|
||
"# print(\"c=\",c)\n",
|
||
"print(\"alpha = \", alpha, \"sur\",b)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 65,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# ça pris > 30 min pour faire la boucle !!!\n",
|
||
"# df7.to_csv(r'/Users/waelbousselmi/Desktop/df7_test.csv', index = False, header=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 75,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"df7 = pd.read_csv('/Users/waelbousselmi/Desktop/df7.csv')\n",
|
||
"#/Users/waelbousselmi/Google Drive/docs_multiples_chocs/doc_MC_juin2021/df13.csv"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 77,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df8 = df7.sort_values(by=['nmarket', 'Subject', 'Period', 'secnd2']) #, ascending = (False) !!! "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 78,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"## très très important (after sorting or concat) !!!!!\n",
|
||
"df9 = df8.reset_index(drop=True)\n",
|
||
"#df9.at[150,\"Subject\"] # pour vérifier"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 104,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"## très important pour remplacer les NaN !!!!\n",
|
||
"df9['risk_test'] = df9['risk_test'].fillna(\"\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 138,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#df9.at[3,\"risk_test\"]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 139,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#len(df9)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 140,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#len(df9[(df9['secnd']== 120)])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 141,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#len(df9[(df9['secnd2']== 120)])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 130,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# pour supprimer les doublants avec eg. secnd2 = 120 deux fois !!!!!!!! (important)\n",
|
||
"df10 = df9[(df9['secnd']== 0) & (df9['secnd2']!= 0)].index\n",
|
||
"df9.drop(df10 , inplace=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 148,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df11 = df9.copy()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 153,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df11['risk_test2']=df11['risk_test']"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 159,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"## très très important (after sorting or concat) !!!!!\n",
|
||
"df12 = df11.reset_index(drop=True) # faut créer un nouveau df si non ça ne marche pas "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 161,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"962\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"a = df12.shape[0]\n",
|
||
"count=0\n",
|
||
"for i in range (0,a):\n",
|
||
" if df12.at[i,'Period'] != \"\":\n",
|
||
" val = df12.at[i,'risk_test2']\n",
|
||
"# print(i,val)\n",
|
||
" count +=1\n",
|
||
" else :\n",
|
||
" df12.at[i,'risk_test2'] = val \n",
|
||
"print(count) "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 162,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>170703_0850</th>\n",
|
||
" <th>nmarket</th>\n",
|
||
" <th>traitement</th>\n",
|
||
" <th>treament</th>\n",
|
||
" <th>Period</th>\n",
|
||
" <th>Subject</th>\n",
|
||
" <th>UpperBound1</th>\n",
|
||
" <th>MedianP1</th>\n",
|
||
" <th>LowerBound1</th>\n",
|
||
" <th>StartWealth</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>GainFinalEUR</th>\n",
|
||
" <th>GainForecastECU</th>\n",
|
||
" <th>GainForecastEUR</th>\n",
|
||
" <th>CompteEpargne</th>\n",
|
||
" <th>CompteEpargneEUR</th>\n",
|
||
" <th>count</th>\n",
|
||
" <th>secnd</th>\n",
|
||
" <th>secnd2</th>\n",
|
||
" <th>risk_test</th>\n",
|
||
" <th>risk_test2</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>170703_0850</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>HH</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>320.0</td>\n",
|
||
" <td>320.0</td>\n",
|
||
" <td>320.0</td>\n",
|
||
" <td>6750.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>21.78</td>\n",
|
||
" <td>1012.50</td>\n",
|
||
" <td>3.00</td>\n",
|
||
" <td>877.50</td>\n",
|
||
" <td>2.60</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>10</td>\n",
|
||
" <td>10</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>170703_0850</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>HH</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>320.0</td>\n",
|
||
" <td>320.0</td>\n",
|
||
" <td>320.0</td>\n",
|
||
" <td>6750.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>21.78</td>\n",
|
||
" <td>1012.50</td>\n",
|
||
" <td>3.00</td>\n",
|
||
" <td>877.50</td>\n",
|
||
" <td>2.60</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td></td>\n",
|
||
" <td>10</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>170703_0850</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>HH</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>320.0</td>\n",
|
||
" <td>320.0</td>\n",
|
||
" <td>320.0</td>\n",
|
||
" <td>6750.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>21.78</td>\n",
|
||
" <td>1012.50</td>\n",
|
||
" <td>3.00</td>\n",
|
||
" <td>877.50</td>\n",
|
||
" <td>2.60</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td></td>\n",
|
||
" <td>10</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>170703_0850</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>HH</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>320.0</td>\n",
|
||
" <td>320.0</td>\n",
|
||
" <td>320.0</td>\n",
|
||
" <td>6750.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>21.78</td>\n",
|
||
" <td>1012.50</td>\n",
|
||
" <td>3.00</td>\n",
|
||
" <td>877.50</td>\n",
|
||
" <td>2.60</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td></td>\n",
|
||
" <td>10</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>170703_0850</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>HH</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>320.0</td>\n",
|
||
" <td>320.0</td>\n",
|
||
" <td>320.0</td>\n",
|
||
" <td>6750.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>21.78</td>\n",
|
||
" <td>1012.50</td>\n",
|
||
" <td>3.00</td>\n",
|
||
" <td>877.50</td>\n",
|
||
" <td>2.60</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td></td>\n",
|
||
" <td>10</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>259339</th>\n",
|
||
" <td>171026_0838</td>\n",
|
||
" <td>18</td>\n",
|
||
" <td>NO</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>15</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>-111.0</td>\n",
|
||
" <td>-111.0</td>\n",
|
||
" <td>-111.0</td>\n",
|
||
" <td>7795.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>23.14</td>\n",
|
||
" <td>2413.44</td>\n",
|
||
" <td>7.15</td>\n",
|
||
" <td>2128.44</td>\n",
|
||
" <td>6.31</td>\n",
|
||
" <td>261355</td>\n",
|
||
" <td>116</td>\n",
|
||
" <td>1796</td>\n",
|
||
" <td></td>\n",
|
||
" <td>3</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>259340</th>\n",
|
||
" <td>171026_0838</td>\n",
|
||
" <td>18</td>\n",
|
||
" <td>NO</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>15</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>-111.0</td>\n",
|
||
" <td>-111.0</td>\n",
|
||
" <td>-111.0</td>\n",
|
||
" <td>7795.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>23.14</td>\n",
|
||
" <td>2413.44</td>\n",
|
||
" <td>7.15</td>\n",
|
||
" <td>2128.44</td>\n",
|
||
" <td>6.31</td>\n",
|
||
" <td>261356</td>\n",
|
||
" <td>117</td>\n",
|
||
" <td>1797</td>\n",
|
||
" <td></td>\n",
|
||
" <td>3</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>259341</th>\n",
|
||
" <td>171026_0838</td>\n",
|
||
" <td>18</td>\n",
|
||
" <td>NO</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>15</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>-111.0</td>\n",
|
||
" <td>-111.0</td>\n",
|
||
" <td>-111.0</td>\n",
|
||
" <td>7795.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>23.14</td>\n",
|
||
" <td>2413.44</td>\n",
|
||
" <td>7.15</td>\n",
|
||
" <td>2128.44</td>\n",
|
||
" <td>6.31</td>\n",
|
||
" <td>261357</td>\n",
|
||
" <td>118</td>\n",
|
||
" <td>1798</td>\n",
|
||
" <td></td>\n",
|
||
" <td>3</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>259342</th>\n",
|
||
" <td>171026_0838</td>\n",
|
||
" <td>18</td>\n",
|
||
" <td>NO</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>15</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>-111.0</td>\n",
|
||
" <td>-111.0</td>\n",
|
||
" <td>-111.0</td>\n",
|
||
" <td>7795.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>23.14</td>\n",
|
||
" <td>2413.44</td>\n",
|
||
" <td>7.15</td>\n",
|
||
" <td>2128.44</td>\n",
|
||
" <td>6.31</td>\n",
|
||
" <td>261358</td>\n",
|
||
" <td>119</td>\n",
|
||
" <td>1799</td>\n",
|
||
" <td></td>\n",
|
||
" <td>3</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>259343</th>\n",
|
||
" <td>171026_0838</td>\n",
|
||
" <td>18</td>\n",
|
||
" <td>NO</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>15</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>-111.0</td>\n",
|
||
" <td>-111.0</td>\n",
|
||
" <td>-111.0</td>\n",
|
||
" <td>7795.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>23.14</td>\n",
|
||
" <td>2413.44</td>\n",
|
||
" <td>7.15</td>\n",
|
||
" <td>2128.44</td>\n",
|
||
" <td>6.31</td>\n",
|
||
" <td>261359</td>\n",
|
||
" <td>120</td>\n",
|
||
" <td>1800</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>259344 rows × 41 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" 170703_0850 nmarket traitement treament Period Subject \\\n",
|
||
"0 170703_0850 1 HH 0 1 1 \n",
|
||
"1 170703_0850 1 HH 0 1 1 \n",
|
||
"2 170703_0850 1 HH 0 1 1 \n",
|
||
"3 170703_0850 1 HH 0 1 1 \n",
|
||
"4 170703_0850 1 HH 0 1 1 \n",
|
||
"... ... ... ... ... ... ... \n",
|
||
"259339 171026_0838 18 NO 2 15 8 \n",
|
||
"259340 171026_0838 18 NO 2 15 8 \n",
|
||
"259341 171026_0838 18 NO 2 15 8 \n",
|
||
"259342 171026_0838 18 NO 2 15 8 \n",
|
||
"259343 171026_0838 18 NO 2 15 8 \n",
|
||
"\n",
|
||
" UpperBound1 MedianP1 LowerBound1 StartWealth ... GainFinalEUR \\\n",
|
||
"0 320.0 320.0 320.0 6750.0 ... 21.78 \n",
|
||
"1 320.0 320.0 320.0 6750.0 ... 21.78 \n",
|
||
"2 320.0 320.0 320.0 6750.0 ... 21.78 \n",
|
||
"3 320.0 320.0 320.0 6750.0 ... 21.78 \n",
|
||
"4 320.0 320.0 320.0 6750.0 ... 21.78 \n",
|
||
"... ... ... ... ... ... ... \n",
|
||
"259339 -111.0 -111.0 -111.0 7795.0 ... 23.14 \n",
|
||
"259340 -111.0 -111.0 -111.0 7795.0 ... 23.14 \n",
|
||
"259341 -111.0 -111.0 -111.0 7795.0 ... 23.14 \n",
|
||
"259342 -111.0 -111.0 -111.0 7795.0 ... 23.14 \n",
|
||
"259343 -111.0 -111.0 -111.0 7795.0 ... 23.14 \n",
|
||
"\n",
|
||
" GainForecastECU GainForecastEUR CompteEpargne CompteEpargneEUR \\\n",
|
||
"0 1012.50 3.00 877.50 2.60 \n",
|
||
"1 1012.50 3.00 877.50 2.60 \n",
|
||
"2 1012.50 3.00 877.50 2.60 \n",
|
||
"3 1012.50 3.00 877.50 2.60 \n",
|
||
"4 1012.50 3.00 877.50 2.60 \n",
|
||
"... ... ... ... ... \n",
|
||
"259339 2413.44 7.15 2128.44 6.31 \n",
|
||
"259340 2413.44 7.15 2128.44 6.31 \n",
|
||
"259341 2413.44 7.15 2128.44 6.31 \n",
|
||
"259342 2413.44 7.15 2128.44 6.31 \n",
|
||
"259343 2413.44 7.15 2128.44 6.31 \n",
|
||
"\n",
|
||
" count secnd secnd2 risk_test risk_test2 \n",
|
||
"0 0 0 0 10 10 \n",
|
||
"1 1 1 1 10 \n",
|
||
"2 2 2 2 10 \n",
|
||
"3 3 3 3 10 \n",
|
||
"4 4 4 4 10 \n",
|
||
"... ... ... ... ... ... \n",
|
||
"259339 261355 116 1796 3 \n",
|
||
"259340 261356 117 1797 3 \n",
|
||
"259341 261357 118 1798 3 \n",
|
||
"259342 261358 119 1799 3 \n",
|
||
"259343 261359 120 1800 0 0 \n",
|
||
"\n",
|
||
"[259344 rows x 41 columns]"
|
||
]
|
||
},
|
||
"execution_count": 162,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df12"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 170,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#df12.to_csv(r'/Users/waelbousselmi/Desktop/df12.csv', index = False, header=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 178,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df13 = df12.rename(columns={'170703_0850': 'ref_date'}) ## pour renommer une colonne\n",
|
||
"#df13.to_stata('/Users/waelbousselmi/Desktop/df13.dta') ### je n'ai pas besoin d'avoir un format dta "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df13['choc1']= \"\"\n",
|
||
"a = df13.shape[0] \n",
|
||
"for i in range (0,a):\n",
|
||
" if (df13.at[i,\"Period\"] > 5 and df13.at[i,\"traitement\"] != \"NO\"):\n",
|
||
" df13.at[i,\"choc1\"] = 1\n",
|
||
" else:\n",
|
||
" df13.at[i,\"choc1\"] = 0"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df13['choc2']= \"\"\n",
|
||
"a = df13.shape[0] \n",
|
||
"for i in range (0,a):\n",
|
||
" if (df13.at[i,\"Period\"] > 10 and df13.at[i,\"traitement\"] != \"NO\"):\n",
|
||
" df13.at[i,\"choc2\"] = 1\n",
|
||
" else:\n",
|
||
" df13.at[i,\"choc2\"] = 0"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df13['choc1_nn']= \"\"\n",
|
||
"a = df13.shape[0] \n",
|
||
"for i in range (0,a):\n",
|
||
" if (df13.at[i,\"Period\"] > 5 and df13.at[i,\"Period\"] < 11 and df13.at[i,\"traitement\"] != \"NO\"):\n",
|
||
" df13.at[i,\"choc1_nn\"] = 1\n",
|
||
" elif (df13.at[i,\"Period\"] <6 and df13.at[i,\"traitement\"] != \"NO\"):\n",
|
||
" df13.at[i,\"choc1_nn\"] = 0"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 186,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df13['choc2']= \"\"\n",
|
||
"a = df13.shape[0] \n",
|
||
"for i in range (0,a):\n",
|
||
" if (df13.at[i,\"Period\"] < 11):\n",
|
||
" df13.at[i,\"choc2\"] = 0\n",
|
||
" else:\n",
|
||
" df13.at[i,\"choc2\"] = 1"
|
||
]
|
||
},
|
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{
|
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"cell_type": "code",
|
||
"execution_count": 4,
|
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|
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|
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"source": []
|
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{
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|
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||
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|
||
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||
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||
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||
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|
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|
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||
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||
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
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|
||
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>259344 rows × 44 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" ref_date nmarket traitement treament Period Subject \\\n",
|
||
"0 170703_0850 1 HH 0 1 1 \n",
|
||
"1 170703_0850 1 HH 0 1 1 \n",
|
||
"2 170703_0850 1 HH 0 1 1 \n",
|
||
"3 170703_0850 1 HH 0 1 1 \n",
|
||
"4 170703_0850 1 HH 0 1 1 \n",
|
||
"... ... ... ... ... ... ... \n",
|
||
"259339 171026_0838 18 NO 2 15 8 \n",
|
||
"259340 171026_0838 18 NO 2 15 8 \n",
|
||
"259341 171026_0838 18 NO 2 15 8 \n",
|
||
"259342 171026_0838 18 NO 2 15 8 \n",
|
||
"259343 171026_0838 18 NO 2 15 8 \n",
|
||
"\n",
|
||
" UpperBound1 MedianP1 LowerBound1 StartWealth ... CompteEpargne \\\n",
|
||
"0 320.0 320.0 320.0 6750.0 ... 877.50 \n",
|
||
"1 320.0 320.0 320.0 6750.0 ... 877.50 \n",
|
||
"2 320.0 320.0 320.0 6750.0 ... 877.50 \n",
|
||
"3 320.0 320.0 320.0 6750.0 ... 877.50 \n",
|
||
"4 320.0 320.0 320.0 6750.0 ... 877.50 \n",
|
||
"... ... ... ... ... ... ... \n",
|
||
"259339 -111.0 -111.0 -111.0 7795.0 ... 2128.44 \n",
|
||
"259340 -111.0 -111.0 -111.0 7795.0 ... 2128.44 \n",
|
||
"259341 -111.0 -111.0 -111.0 7795.0 ... 2128.44 \n",
|
||
"259342 -111.0 -111.0 -111.0 7795.0 ... 2128.44 \n",
|
||
"259343 -111.0 -111.0 -111.0 7795.0 ... 2128.44 \n",
|
||
"\n",
|
||
" CompteEpargneEUR count secnd secnd2 risk_test risk_test2 choc1 \\\n",
|
||
"0 2.60 0 0 0 10.0 10.0 0 \n",
|
||
"1 2.60 1 1 1 NaN 10.0 0 \n",
|
||
"2 2.60 2 2 2 NaN 10.0 0 \n",
|
||
"3 2.60 3 3 3 NaN 10.0 0 \n",
|
||
"4 2.60 4 4 4 NaN 10.0 0 \n",
|
||
"... ... ... ... ... ... ... ... \n",
|
||
"259339 6.31 261355 116 1796 NaN 3.0 1 \n",
|
||
"259340 6.31 261356 117 1797 NaN 3.0 1 \n",
|
||
"259341 6.31 261357 118 1798 NaN 3.0 1 \n",
|
||
"259342 6.31 261358 119 1799 NaN 3.0 1 \n",
|
||
"259343 6.31 261359 120 1800 0.0 0.0 1 \n",
|
||
"\n",
|
||
" choc2 newid \n",
|
||
"0 0 1 \n",
|
||
"1 0 1 \n",
|
||
"2 0 1 \n",
|
||
"3 0 1 \n",
|
||
"4 0 1 \n",
|
||
"... ... ... \n",
|
||
"259339 1 144 \n",
|
||
"259340 1 144 \n",
|
||
"259341 1 144 \n",
|
||
"259342 1 144 \n",
|
||
"259343 1 144 \n",
|
||
"\n",
|
||
"[259344 rows x 44 columns]"
|
||
]
|
||
},
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#df14 = df13.groupby('secnd2').risk_test2.mean()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#df14"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# calcul interval de confiance (autre méthode de calcul) \n",
|
||
"#import numpy as np\n",
|
||
"#import scipy.stats as st\n",
|
||
"#data = df14\n",
|
||
"#st.t.interval(alpha=0.95, df=len(data)-1, loc=np.mean(data), scale=st.sem(data)) "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 190,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# nouvelle variable \"newid\", id pour chaque sujet \n",
|
||
"df13['newid']= \"\"\n",
|
||
"a = df13.shape[0]\n",
|
||
"var = 1\n",
|
||
"cc = df13.at[0,\"Subject\"]\n",
|
||
"for i in range (0,a):\n",
|
||
" if (df13.at[i,\"Subject\"] == cc):\n",
|
||
" df13.at[i,\"newid\"] = var\n",
|
||
" else:\n",
|
||
" cc = df13.at[i,\"Subject\"]\n",
|
||
" var += 1\n",
|
||
" df13.at[i,\"newid\"] = var"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"df13 = pd.read_csv('/Users/waelbousselmi/Google Drive/docs_multiples_chocs/doc_MC_juin2021/df13.csv')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df13.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"dfhh = df13[(df13[\"traitement\"] == \"HH\")]\n",
|
||
"dfbb = df13[(df13[\"traitement\"] == \"BB\")]\n",
|
||
"dft0 = df13[(df13[\"traitement\"] == \"NO\")]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# pour créer deux groupe par traitement (groupe 1: ceux qui sont plus averses au risque et \n",
|
||
"#groupe 2: ceux qui sont plus amateurs de risque)\n",
|
||
"import statistics as st\n",
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"median1 = st.median(dfhh[\"risk_test2\"])\n",
|
||
"median2 = st.median(dfbb[\"risk_test2\"])\n",
|
||
"median3 = st.median(dft0[\"risk_test2\"])\n",
|
||
"dfhh_g1 = dfhh[(dfhh[\"risk_test2\"] < median1)]\n",
|
||
"dfhh_g2 = dfhh[(dfhh[\"risk_test2\"] >= median1)]\n",
|
||
"dfbb_g1 = dfbb[(dfbb[\"risk_test2\"] < median2)]\n",
|
||
"dfbb_g2 = dfbb[(dfbb[\"risk_test2\"] >= median2)]\n",
|
||
"dft0_g1 = dft0[(dft0[\"risk_test2\"] < median3)]\n",
|
||
"dft0_g2 = dft0[(dft0[\"risk_test2\"] >= median3)]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# pour calculer l'interval de confiance à 95%\n",
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"import math\n",
|
||
"\n",
|
||
"stats_hh = dfhh.groupby(['secnd2'])['risk_test2'].agg(['mean', 'count', 'std'])\n",
|
||
"stats_bb = dfbb.groupby(['secnd2'])['risk_test2'].agg(['mean', 'count', 'std'])\n",
|
||
"stats_t0 = dft0.groupby(['secnd2'])['risk_test2'].agg(['mean', 'count', 'std'])\n",
|
||
"\n",
|
||
"stats_hh_g1 = dfhh_g1.groupby(['secnd2'])['risk_test2'].agg(['mean', 'count', 'std'])\n",
|
||
"stats_bb_g1 = dfbb_g1.groupby(['secnd2'])['risk_test2'].agg(['mean', 'count', 'std'])\n",
|
||
"stats_t0_g1 = dft0_g1.groupby(['secnd2'])['risk_test2'].agg(['mean', 'count', 'std'])\n",
|
||
"\n",
|
||
"stats_hh_g2 = dfhh_g2.groupby(['secnd2'])['risk_test2'].agg(['mean', 'count', 'std'])\n",
|
||
"stats_bb_g2 = dfbb_g2.groupby(['secnd2'])['risk_test2'].agg(['mean', 'count', 'std'])\n",
|
||
"stats_t0_g2 = dft0_g2.groupby(['secnd2'])['risk_test2'].agg(['mean', 'count', 'std'])\n",
|
||
"\n",
|
||
"\n",
|
||
"ci95_hi_hh = []\n",
|
||
"ci95_lo_hh = []\n",
|
||
"ci95_hi_bb = []\n",
|
||
"ci95_lo_bb = []\n",
|
||
"ci95_hi_t0 = []\n",
|
||
"ci95_lo_t0 = []\n",
|
||
"\n",
|
||
"ci95_hi_hh_g1 = []\n",
|
||
"ci95_lo_hh_g1 = []\n",
|
||
"ci95_hi_bb_g1 = []\n",
|
||
"ci95_lo_bb_g1 = []\n",
|
||
"ci95_hi_t0_g1 = []\n",
|
||
"ci95_lo_t0_g1 = []\n",
|
||
"\n",
|
||
"ci95_hi_hh_g2 = []\n",
|
||
"ci95_lo_hh_g2 = []\n",
|
||
"ci95_hi_bb_g2 = []\n",
|
||
"ci95_lo_bb_g2 = []\n",
|
||
"ci95_hi_t0_g2 = []\n",
|
||
"ci95_lo_t0_g2 = []\n",
|
||
"\n",
|
||
"for i in stats_hh.index:\n",
|
||
" m, c, s = stats_hh.loc[i]\n",
|
||
" ci95_hi_hh.append(m + 1.95*s/math.sqrt(c))\n",
|
||
" ci95_lo_hh.append(m - 1.95*s/math.sqrt(c))\n",
|
||
"\n",
|
||
"for i in stats_bb.index:\n",
|
||
" m1, c1, s1 = stats_bb.loc[i]\n",
|
||
" ci95_hi_bb.append(m1 + 1.95*s1/math.sqrt(c1))\n",
|
||
" ci95_lo_bb.append(m1 - 1.95*s1/math.sqrt(c1))\n",
|
||
"\n",
|
||
"for i in stats_t0.index:\n",
|
||
" m2, c2, s2 = stats_t0.loc[i]\n",
|
||
" ci95_hi_t0.append(m2 + 1.95*s2/math.sqrt(c2))\n",
|
||
" ci95_lo_t0.append(m2 - 1.95*s2/math.sqrt(c2))\n",
|
||
"\n",
|
||
" \n",
|
||
"for i in stats_hh_g1.index:\n",
|
||
" m_g1_g1, c_g1, s_g1 = stats_hh_g1.loc[i]\n",
|
||
" ci95_hi_hh_g1.append(m_g1 + 1.95*s_g1/math.sqrt(c_g1))\n",
|
||
" ci95_lo_hh_g1.append(m_g1 - 1.95*s_g1/math.sqrt(c_g1))\n",
|
||
"\n",
|
||
"for i in stats_bb_g1.index:\n",
|
||
" m1_g1, c1_g1, s1_g1 = stats_bb_g1.loc[i]\n",
|
||
" ci95_hi_bb_g1.append(m1_g1 + 1.95*s1_g1/math.sqrt(c1_g1))\n",
|
||
" ci95_lo_bb_g1.append(m1_g1 - 1.95*s1_g1/math.sqrt(c1_g1))\n",
|
||
"\n",
|
||
"for i in stats_t0_g1.index:\n",
|
||
" m2_g1, c2_g1, s2_g1 = stats_t0_g1.loc[i]\n",
|
||
" ci95_hi_t0_g1.append(m2_g1 + 1.95*s2_g1/math.sqrt(c2_g1))\n",
|
||
" ci95_lo_t0_g1.append(m2_g1 - 1.95*s2_g1/math.sqrt(c2_g1))\n",
|
||
"\n",
|
||
" \n",
|
||
"for i in stats_hh_g2.index:\n",
|
||
" m_g2, c_g2, s_g2 = stats_hh_g2.loc[i]\n",
|
||
" ci95_hi_hh_g2.append(m_g2 + 1.95*s_g2/math.sqrt(c_g2))\n",
|
||
" ci95_lo_hh_g2.append(m_g2 - 1.95*s_g2/math.sqrt(c_g2))\n",
|
||
"\n",
|
||
"for i in stats_bb_g2.index:\n",
|
||
" m1_g2, c1_g2, s1_g2 = stats_bb_g2.loc[i]\n",
|
||
" ci95_hi_bb_g2.append(m1_g2 + 1.95*s1_g2/math.sqrt(c1_g2))\n",
|
||
" ci95_lo_bb_g2.append(m1_g2 - 1.95*s1_g2/math.sqrt(c1_g2))\n",
|
||
"\n",
|
||
"for i in stats_t0_g2.index:\n",
|
||
" m2_g2, c2_g2, s2_g2 = stats_t0_g2.loc[i]\n",
|
||
" ci95_hi_t0_g2.append(m2_g2 + 1.95*s2_g2/math.sqrt(c2_g2))\n",
|
||
" ci95_lo_t0_g2.append(m2_g2 - 1.95*s2_g2/math.sqrt(c2_g2))\n",
|
||
"\n",
|
||
" \n",
|
||
"stats_hh['ci95_hi_hh'] = ci95_hi_hh\n",
|
||
"stats_hh['ci95_lo_hh'] = ci95_lo_hh\n",
|
||
"stats_bb['ci95_hi_bb'] = ci95_hi_bb\n",
|
||
"stats_bb['ci95_lo_bb'] = ci95_lo_bb\n",
|
||
"stats_t0['ci95_hi_t0'] = ci95_hi_t0\n",
|
||
"stats_t0['ci95_lo_t0'] = ci95_lo_t0\n",
|
||
"\n",
|
||
"stats_hh_g1['ci95_hi_hh_g1'] = ci95_hi_hh_g1\n",
|
||
"stats_hh_g1['ci95_lo_hh_g1'] = ci95_lo_hh_g1\n",
|
||
"stats_bb_g1['ci95_hi_bb_g1'] = ci95_hi_bb_g1\n",
|
||
"stats_bb_g1['ci95_lo_bb_g1'] = ci95_lo_bb_g1\n",
|
||
"stats_t0_g1['ci95_hi_t0_g1'] = ci95_hi_t0_g1\n",
|
||
"stats_t0_g1['ci95_lo_t0_g1'] = ci95_lo_t0_g1\n",
|
||
"\n",
|
||
"stats_hh_g2['ci95_hi_hh_g2'] = ci95_hi_hh_g2\n",
|
||
"stats_hh_g2['ci95_lo_hh_g2'] = ci95_lo_hh_g2\n",
|
||
"stats_bb_g2['ci95_hi_bb_g2'] = ci95_hi_bb_g2\n",
|
||
"stats_bb_g2['ci95_lo_bb_g2'] = ci95_lo_bb_g2\n",
|
||
"stats_t0_g2['ci95_hi_t0_g2'] = ci95_hi_t0_g2\n",
|
||
"stats_t0_g2['ci95_lo_t0_g2'] = ci95_lo_t0_g2\n",
|
||
"\n",
|
||
"#print(stats)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 720x720 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import matplotlib.patches as mpatches\n",
|
||
"from matplotlib.colors import colorConverter as cc\n",
|
||
"import numpy as np\n",
|
||
"from matplotlib.collections import LineCollection\n",
|
||
" \n",
|
||
"def plot_mean_and_CI(mean, lb, ub, color_mean=None, color_shading=None):\n",
|
||
" # plot the shaded range of the confidence intervals\n",
|
||
" plt.fill_between(range(mean.shape[0]), ub, lb,\n",
|
||
" color=color_shading, alpha=.3)\n",
|
||
" # plot the mean on top\n",
|
||
" plt.plot(mean, color_mean, lw=5)\n",
|
||
" \n",
|
||
"# generate 3 sets of random means and confidence intervals to plot\n",
|
||
"mean0 = stats_hh[\"mean\"]\n",
|
||
"ub0 = stats_hh['ci95_hi_hh']\n",
|
||
"lb0 = stats_hh['ci95_lo_hh']\n",
|
||
" \n",
|
||
"mean1 = stats_bb[\"mean\"]\n",
|
||
"ub1 = stats_bb['ci95_hi_bb']\n",
|
||
"lb1 = stats_bb['ci95_lo_bb']\n",
|
||
" \n",
|
||
"mean2 = stats_t0[\"mean\"]\n",
|
||
"ub2 = stats_t0['ci95_hi_t0']\n",
|
||
"lb2 = stats_t0['ci95_lo_t0']\n",
|
||
" \n",
|
||
" \n",
|
||
"# plot the data\n",
|
||
"fig = plt.figure(1, figsize=(10, 10))\n",
|
||
"plot_mean_and_CI(mean0, ub0, lb0, color_mean='g', color_shading='green')\n",
|
||
"plot_mean_and_CI(mean1, ub1, lb1, color_mean='r', color_shading='red')\n",
|
||
"plot_mean_and_CI(mean2, ub2, lb2, color_mean='b', color_shading='blue')\n",
|
||
" \n",
|
||
"class LegendObject(object):\n",
|
||
" def __init__(self, facecolor='red', edgecolor='white', dashed=False):\n",
|
||
" self.facecolor = facecolor\n",
|
||
" self.edgecolor = edgecolor\n",
|
||
" self.dashed = dashed\n",
|
||
" \n",
|
||
" def legend_artist(self, legend, orig_handle, fontsize, handlebox):\n",
|
||
" x0, y0 = handlebox.xdescent, handlebox.ydescent\n",
|
||
" width, height = handlebox.width, handlebox.height\n",
|
||
" patch = mpatches.Rectangle(\n",
|
||
" # create a rectangle that is filled with color\n",
|
||
" [x0, y0], width, height, facecolor=self.facecolor,\n",
|
||
" # and whose edges are the faded color\n",
|
||
" edgecolor=self.edgecolor, lw=3)\n",
|
||
" handlebox.add_artist(patch)\n",
|
||
" \n",
|
||
" # if we're creating the legend for a dashed line,\n",
|
||
" # manually add the dash in to our rectangle\n",
|
||
" if self.dashed:\n",
|
||
" patch1 = mpatches.Rectangle(\n",
|
||
" [x0 + 2*width/5, y0], width/5, height, facecolor=self.edgecolor,\n",
|
||
" transform=handlebox.get_transform())\n",
|
||
" handlebox.add_artist(patch1)\n",
|
||
" \n",
|
||
" return patch\n",
|
||
" \n",
|
||
"bg = np.array([1, 1, 1]) # background of the legend is white\n",
|
||
"colors = ['g', 'r', 'b']\n",
|
||
"# with alpha = .5, the faded color is the average of the background and color\n",
|
||
"colors_faded = [(np.array(cc.to_rgb(color)) + bg) / 2.0 for color in colors]\n",
|
||
" \n",
|
||
"plt.legend([0, 1, 2], ['Up-Up', 'Down-Down', 'T0'],\n",
|
||
" handler_map={\n",
|
||
" 0: LegendObject(colors[0], colors_faded[0]),\n",
|
||
" 1: LegendObject(colors[1], colors_faded[1]),\n",
|
||
" 2: LegendObject(colors[2], colors_faded[2], dashed=True),\n",
|
||
" })\n",
|
||
" \n",
|
||
"plt.title('Mean risk preferences by second')\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.ylim(3,6)\n",
|
||
"ax = plt.gca()\n",
|
||
"#ax.axes.xaxis.set_visible(False)\n",
|
||
"ax.xaxis.set_ticks(np.arange(0, 1800, 120))\n",
|
||
"#ax.set_xticks(ax.get_xticks()[::120])\n",
|
||
"#plt.xlim(0,1800)\n",
|
||
"\n",
|
||
"#plt.grid()\n",
|
||
"\n",
|
||
"l3 = list(range(120,1800,120))\n",
|
||
"plt.vlines(x=l3, ymin=0, ymax=10, colors='purple', ls='--', lw=1)\n",
|
||
"\n",
|
||
"\n",
|
||
"# les deux lignes verticales pour choc1 et choc2\n",
|
||
"l1 = [(600,0 ), (600, 10)]\n",
|
||
"l2 = [(1200,0), (1200, 10)]\n",
|
||
"lc = LineCollection([l1, l2], color=[\"k\",\"k\"], lw=2)\n",
|
||
"plt.gca().add_collection(lc)\n",
|
||
"\n",
|
||
"#\n",
|
||
"\n",
|
||
"plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 55,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"l3 = list(range(120,1800,120))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 56,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"[120, 240, 360, 480, 600, 720, 840, 960, 1080, 1200, 1320, 1440, 1560, 1680]"
|
||
]
|
||
},
|
||
"execution_count": 56,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"l3"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 53,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"[120, 240, 360, 480, 600, 720, 840, 960, 1080, 1200, 1320, 1440, 1560, 1680]"
|
||
]
|
||
},
|
||
"execution_count": 53,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 47,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"from matplotlib.collections import LineCollection\n",
|
||
"\n",
|
||
"np.random.seed(5)\n",
|
||
"x = np.arange(1, 101)\n",
|
||
"y = 20 + 3 * x + np.random.normal(0, 60, 100)\n",
|
||
"plt.plot(x, y, \"o\")\n",
|
||
"\n",
|
||
"# Takes list of lines, where each line is a sequence of coordinates\n",
|
||
"l1 = [(70, 100), (70, 250)]\n",
|
||
"l2 = [(70, 90), (90, 200)]\n",
|
||
"lc = LineCollection([l1, l2], color=[\"k\",\"blue\"], lw=2)\n",
|
||
"\n",
|
||
"plt.gca().add_collection(lc)\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"fig = plt.figure()\n",
|
||
"ax = fig.add_subplot(111)\n",
|
||
"\n",
|
||
"# you can change each line separately, like:\n",
|
||
"#ax.spines['right'].set_linewidth(0.5)\n",
|
||
"# to change all, just write:\n",
|
||
"\n",
|
||
"for axis in ['top','bottom','left','right']:\n",
|
||
" ax.spines[axis].set_linewidth(2)\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 201,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#df13.to_csv(r'/Users/waelbousselmi/Desktop/df13.csv', index = False, header=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 204,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df14_1 = df14[(df14[\"nmarket\"] == 1)]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 205,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x432 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"fig, ax = plt.subplots(figsize=(15,6))\n",
|
||
"for name, group in df14_1.groupby('Subject'):\n",
|
||
" group.plot(x='secnd2',y='risk_test2', ax=ax, label=name,title='Plot by Month')\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 202,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 1080x432 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"fig, ax = plt.subplots(figsize=(15,6))\n",
|
||
"for name, group in df14.groupby('nmarket'):\n",
|
||
" group.plot(x='secnd2',y='risk_test2', ax=ax, label=name,title='Plot by Month')\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 208,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#import seaborn as sns"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 223,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#sns.lineplot(data=df14_1, x=\"secnd2\", y=\"risk_test2\", hue=\"Subject\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 224,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"1"
|
||
]
|
||
},
|
||
"execution_count": 224,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df14_1.at[2, \"Subject\"]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 237,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"may_flights = df14_1.query(\"Subject == '5'\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 238,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
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"<div>\n",
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"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
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|
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" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>ref_date</th>\n",
|
||
" <th>nmarket</th>\n",
|
||
" <th>traitement</th>\n",
|
||
" <th>treament</th>\n",
|
||
" <th>Period</th>\n",
|
||
" <th>Subject</th>\n",
|
||
" <th>UpperBound1</th>\n",
|
||
" <th>MedianP1</th>\n",
|
||
" <th>LowerBound1</th>\n",
|
||
" <th>StartWealth</th>\n",
|
||
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|
||
" <th>CompteEpargneEUR</th>\n",
|
||
" <th>count</th>\n",
|
||
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|
||
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|
||
" <th>risk_test</th>\n",
|
||
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|
||
" <th>choc1</th>\n",
|
||
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|
||
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|
||
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||
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|
||
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|
||
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||
" <tr>\n",
|
||
" <th>7204</th>\n",
|
||
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|
||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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|
||
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|
||
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||
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|
||
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|
||
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|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7206</th>\n",
|
||
" <td>170703_0850</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>HH</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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||
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||
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||
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||
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||
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|
||
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|
||
" <tr>\n",
|
||
" <th>7207</th>\n",
|
||
" <td>170703_0850</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>HH</td>\n",
|
||
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|
||
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|
||
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||
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|
||
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|
||
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|
||
" <td>6750.0</td>\n",
|
||
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|
||
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|
||
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|
||
" <td>3</td>\n",
|
||
" <td>3</td>\n",
|
||
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|
||
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|
||
" <td>0</td>\n",
|
||
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|
||
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|
||
" <td>0</td>\n",
|
||
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|
||
" <tr>\n",
|
||
" <th>7208</th>\n",
|
||
" <td>170703_0850</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>HH</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>6750.0</td>\n",
|
||
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|
||
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|
||
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||
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||
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||
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||
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9000</th>\n",
|
||
" <td>170703_0850</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>HH</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>15</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
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|
||
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|
||
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|
||
" <td>14152</td>\n",
|
||
" <td>116</td>\n",
|
||
" <td>1796</td>\n",
|
||
" <td></td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>1</td>\n",
|
||
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|
||
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|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9001</th>\n",
|
||
" <td>170703_0850</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>HH</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>-1.89</td>\n",
|
||
" <td>14153</td>\n",
|
||
" <td>117</td>\n",
|
||
" <td>1797</td>\n",
|
||
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|
||
" <td>5</td>\n",
|
||
" <td>1</td>\n",
|
||
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|
||
" <td>5</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9002</th>\n",
|
||
" <td>170703_0850</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>HH</td>\n",
|
||
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|
||
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|
||
" <td>5</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>118</td>\n",
|
||
" <td>1798</td>\n",
|
||
" <td></td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9003</th>\n",
|
||
" <td>170703_0850</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>HH</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>15</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>10369.9</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-1.89</td>\n",
|
||
" <td>14155</td>\n",
|
||
" <td>119</td>\n",
|
||
" <td>1799</td>\n",
|
||
" <td></td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9004</th>\n",
|
||
" <td>170703_0850</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>HH</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>120</td>\n",
|
||
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|
||
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|
||
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|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>1801 rows × 45 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" ref_date nmarket traitement treament Period Subject UpperBound1 \\\n",
|
||
"7204 170703_0850 1 HH 0 1 5 350.0 \n",
|
||
"7205 170703_0850 1 HH 0 1 5 350.0 \n",
|
||
"7206 170703_0850 1 HH 0 1 5 350.0 \n",
|
||
"7207 170703_0850 1 HH 0 1 5 350.0 \n",
|
||
"7208 170703_0850 1 HH 0 1 5 350.0 \n",
|
||
"... ... ... ... ... ... ... ... \n",
|
||
"9000 170703_0850 1 HH 0 15 5 0.0 \n",
|
||
"9001 170703_0850 1 HH 0 15 5 0.0 \n",
|
||
"9002 170703_0850 1 HH 0 15 5 0.0 \n",
|
||
"9003 170703_0850 1 HH 0 15 5 0.0 \n",
|
||
"9004 170703_0850 1 HH 0 15 5 0.0 \n",
|
||
"\n",
|
||
" MedianP1 LowerBound1 StartWealth ... CompteEpargneEUR count secnd \\\n",
|
||
"7204 300.0 250.0 6750.0 ... 1.27 484 0 \n",
|
||
"7205 300.0 250.0 6750.0 ... 1.27 485 1 \n",
|
||
"7206 300.0 250.0 6750.0 ... 1.27 486 2 \n",
|
||
"7207 300.0 250.0 6750.0 ... 1.27 487 3 \n",
|
||
"7208 300.0 250.0 6750.0 ... 1.27 488 4 \n",
|
||
"... ... ... ... ... ... ... ... \n",
|
||
"9000 0.0 0.0 10369.9 ... -1.89 14152 116 \n",
|
||
"9001 0.0 0.0 10369.9 ... -1.89 14153 117 \n",
|
||
"9002 0.0 0.0 10369.9 ... -1.89 14154 118 \n",
|
||
"9003 0.0 0.0 10369.9 ... -1.89 14155 119 \n",
|
||
"9004 0.0 0.0 10369.9 ... -1.89 14156 120 \n",
|
||
"\n",
|
||
" secnd2 risk_test risk_test2 choc1 choc2 newid choc1_nn \n",
|
||
"7204 0 5 5 0 0 5 0 \n",
|
||
"7205 1 5 0 0 5 0 \n",
|
||
"7206 2 5 0 0 5 0 \n",
|
||
"7207 3 5 0 0 5 0 \n",
|
||
"7208 4 5 0 0 5 0 \n",
|
||
"... ... ... ... ... ... ... ... \n",
|
||
"9000 1796 5 1 1 5 1 \n",
|
||
"9001 1797 5 1 1 5 1 \n",
|
||
"9002 1798 5 1 1 5 1 \n",
|
||
"9003 1799 5 1 1 5 1 \n",
|
||
"9004 1800 5 1 1 5 1 \n",
|
||
"\n",
|
||
"[1801 rows x 45 columns]"
|
||
]
|
||
},
|
||
"execution_count": 238,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"may_flights"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 244,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import statistics as st\n",
|
||
"v11 = st.mean(may_flights.risk_test2)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 245,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"5.021654636313159"
|
||
]
|
||
},
|
||
"execution_count": 245,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"v11"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 243,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<AxesSubplot:xlabel='secnd2', ylabel='secnd'>"
|
||
]
|
||
},
|
||
"execution_count": 243,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"sns.lineplot(data=may_flights, x=\"secnd2\", y=\"secnd\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 211,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"flights_wide = df14_1.pivot(\"secnd2\", \"Subject\", \"risk_test2\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 217,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th>Subject</th>\n",
|
||
" <th>1</th>\n",
|
||
" <th>2</th>\n",
|
||
" <th>3</th>\n",
|
||
" <th>4</th>\n",
|
||
" <th>5</th>\n",
|
||
" <th>6</th>\n",
|
||
" <th>7</th>\n",
|
||
" <th>8</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>secnd2</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>10</td>\n",
|
||
" <td>2.2</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>2.5</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>10</td>\n",
|
||
" <td>2.2</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>2.5</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>10</td>\n",
|
||
" <td>2.2</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>2.5</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>10</td>\n",
|
||
" <td>2.2</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>2.5</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>10</td>\n",
|
||
" <td>2.2</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>2.5</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1796</th>\n",
|
||
" <td>10</td>\n",
|
||
" <td>7.12</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>10</td>\n",
|
||
" <td>10</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1797</th>\n",
|
||
" <td>10</td>\n",
|
||
" <td>7.12</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>10</td>\n",
|
||
" <td>10</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1798</th>\n",
|
||
" <td>10</td>\n",
|
||
" <td>7.12</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>10</td>\n",
|
||
" <td>10</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1799</th>\n",
|
||
" <td>10</td>\n",
|
||
" <td>7.12</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>10</td>\n",
|
||
" <td>10</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1800</th>\n",
|
||
" <td>10</td>\n",
|
||
" <td>7.12</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>10</td>\n",
|
||
" <td>10</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>1801 rows × 8 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
"Subject 1 2 3 4 5 6 7 8\n",
|
||
"secnd2 \n",
|
||
"0 10 2.2 1 2 5 5 5 2.5\n",
|
||
"1 10 2.2 1 2 5 5 5 2.5\n",
|
||
"2 10 2.2 1 2 5 5 5 2.5\n",
|
||
"3 10 2.2 1 2 5 5 5 2.5\n",
|
||
"4 10 2.2 1 2 5 5 5 2.5\n",
|
||
"... .. ... .. .. .. .. .. ...\n",
|
||
"1796 10 7.12 1 2 5 8 10 10\n",
|
||
"1797 10 7.12 1 2 5 8 10 10\n",
|
||
"1798 10 7.12 1 2 5 8 10 10\n",
|
||
"1799 10 7.12 1 2 5 8 10 10\n",
|
||
"1800 10 7.12 1 2 5 8 10 10\n",
|
||
"\n",
|
||
"[1801 rows x 8 columns]"
|
||
]
|
||
},
|
||
"execution_count": 217,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"flights_wide"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# https://seaborn.pydata.org/generated/seaborn.lineplot.html"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"#sns.lineplot(data=flights_wide[\"Subject\"])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"fig, ax = plt.subplots(figsize=(15,6))\n",
|
||
"for name, group in df14_1.groupby('Subject'):\n",
|
||
" group.plot(x='secnd2',y='risk_test2', ax=ax, label=name,title='Plot by Month')\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.8.5"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 4
|
||
}
|