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testgit
2025-03-06 10:28:01 +01:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"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": {
"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>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",
" <th>...</th>\n",
" <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",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>170703_0850</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.864</td>\n",
" <td>4.088</td>\n",
" <td>3.75</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>170703_0850</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.076</td>\n",
" <td>4.938</td>\n",
" <td>5.00</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>170703_0850</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.170</td>\n",
" <td>4.475</td>\n",
" <td>3.75</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>170703_0850</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.595</td>\n",
" <td>5.688</td>\n",
" <td>6.00</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>170703_0850</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.753</td>\n",
" <td>5.075</td>\n",
" <td>3.75</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 65 columns</p>\n",
"</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]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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>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",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-10.00</td>\n",
" <td>3.333333</td>\n",
" <td>-3.333333</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.00</td>\n",
" <td>1.666667</td>\n",
" <td>-1.666667</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5.00</td>\n",
" <td>1.666667</td>\n",
" <td>-0.555556</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>10.00</td>\n",
" <td>2.083333</td>\n",
" <td>0.416667</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.05</td>\n",
" <td>2.003333</td>\n",
" <td>0.670000</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</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": []
},
{
"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>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"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": "iVBORw0KGgoAAAANSUhEUgAAAdIAAAE/CAYAAADyukJqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAO50lEQVR4nO3dbYyld1nH8d9lF9QKBswOQihlwQAJEBLIQEAiIoipSKgveNFGEJFkA4kIRoMgiZV3RAk+JEazgQoGUqJQkRBQGgQbE6huS4GW5UlEWKh2sJEHMdaGyxc7xLLMdKbnOrszp+fzSSYzc849577m5l++uc+cc291dwCAxXzfQQ8AAKtMSAFgQEgBYEBIAWBASAFgQEgBYODI+dzZ0aNH+9ixY+dzlwAwdv3113+1uzd2uu+8hvTYsWM5efLk+dwlAIxV1b/udp+ndgFgQEgBYEBIAWBgz5BW1ZVVdWtV3XTW7S+rqk9X1c1V9bvnbkQAOLz2c0b65iSX3PmGqvqpJJcmeVx3PybJ65c/GgAcfnuGtLuvTXLbWTe/NMnruvt/tre59RzMBgCH3qJ/I31kkp+oquuq6u+r6onLHAoAVsWi7yM9kuT+SZ6c5IlJ/qKqHt47/OOmVXU8yfEkufjiixedEwAOpUXPSE8nubrP+Mck305ydKcNu/tEd2929+bGxo4XhQCAlbVoSN+V5BlJUlWPTHLvJF9d0kwAsDL2fGq3qq5K8vQkR6vqdJIrklyZ5Mrtt8TcnuSFOz2tCwD3dHuGtLsv3+Wu5y95FgBYOef1ovUA3INVHfQE/+88PknqEoEAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMLBnSKvqyqq6tapu2uG+36iqrqqj52Y8ADjc9nNG+uYkl5x9Y1U9JMmzknxxyTMBwMrYM6TdfW2S23a46/eTvDJJL3soAFgVC/2NtKqem+TL3f2xJc8DACvlyN39gaq6MMlrkvzMPrc/nuR4klx88cV3d3cAcKgtckb6Y0keluRjVfWFJBcluaGqHrjTxt19ors3u3tzY2Nj8UkB4BC622ek3f2JJA/4zvfbMd3s7q8ucS4AWAn7efvLVUk+nORRVXW6ql587scCgNWw5xlpd1++x/3HljYNAKwYVzYCgAEhBYABIQWAASEFgAEhBYABIQWAASEFgAEhBYABIQWAASEFgAEhBYABIQWAASEFgAEhBYABIQWAASEFgAEhBYABIQWAASEFgAEhBYABIQWAASEFgAEhBYABIQWAASEFgAEhBYCBPUNaVVdW1a1VddOdbvu9qvpUVX28qv6qqu53TqcEgENqP2ekb05yyVm3XZPksd39uCSfSfLqJc8FACthz5B297VJbjvrtvd39x3b334kyUXnYDYAOPSW8TfSX07yviU8DgCsnFFIq+o1Se5I8ra72OZ4VZ2sqpNbW1uT3QHAobNwSKvqhUmek+QXurt32667T3T3ZndvbmxsLLo7ADiUjizyQ1V1SZLfTPKT3f2t5Y4EAKtjz5BW1VVJnp7kaFWdTnJFzrxK9/uTXFNVSfKR7n7JOZwTlubMkj08dn8+B1gFe4a0uy/f4eY3nYNZAGDluLIRAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwsdNF6gHXkOs3sxBkpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADOwZ0qq6sqpuraqb7nTbj1TVNVX12e3P9z+3YwLA4bSfM9I3J7nkrNteleQD3f2IJB/Y/h4A1s6eIe3ua5PcdtbNlyZ5y/bXb0ny88sdCwBWw6J/I/3R7r4lSbY/P2C3DavqeFWdrKqTW1tbC+4OAA6nc/5io+4+0d2b3b25sbFxrncHAOfVoiH996p6UJJsf751eSMBwOo4suDPvTvJC5O8bvvzXy9tIgD2pV5bBz3Cd+mDHuCA7OftL1cl+XCSR1XV6ap6cc4E9FlV9dkkz9r+HgDWzp5npN19+S53PXPJswDAynFlIwAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBiFtKp+rapurqqbquqqqvqBZQ0GAKtg4ZBW1YOT/GqSze5+bJILkly2rMEAYBVMn9o9kuQHq+pIkguTfGU+EgCsjoVD2t1fTvL6JF9MckuSr3X3+8/erqqOV9XJqjq5tbW1+KQAcAgdWfQHq+r+SS5N8rAk/5nkL6vq+d391jtv190nkpxIks3NzV58VLhnqtfWQY/wXfoK/5nC3TF5avenk/xLd2919/8muTrJjy9nLABYDZOQfjHJk6vqwqqqJM9Mcmo5YwHAapj8jfS6JO9IckOST2w/1oklzQUAK2Hhv5EmSXdfkeSKJc0CACvHlY0AYEBIAWBASAFgQEgBYEBIAWBASAFgQEgBYEBIAWBASAFgQEgBYEBIAWBASAFgQEgBYEBIAWBASAFgQEgBYGD0D3sD90BVBz3Bd+s+6AngLjkjBYABIQWAASEFgAEhBYABIQWAASEFgAEhBYABIQWAASEFgIFRSKvqflX1jqr6VFWdqqqnLGswAFgF00sE/mGSv+nu51XVvZNcuISZAGBlLBzSqvrhJE9L8ktJ0t23J7l9OWMBwGqYPLX78CRbSf6sqj5aVW+sqh86e6OqOl5VJ6vq5NbW1mB3AHD4TEJ6JMkTkvxJdz8+yX8ledXZG3X3ie7e7O7NjY2Nwe4A4PCZhPR0ktPdfd329+/ImbACwNpYOKTd/W9JvlRVj9q+6ZlJPrmUqQBgRUxftfuyJG/bfsXu55O8aD4SS+EfZwY4L0Yh7e4bk2wuZxQAWD2ubAQAA0IKAANCCgADQgoAA0IKAANCCgAD0/eRsq1ee7jet+ldmwDnhzNSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGFjpkFYdng8A1tNKhxQADpqQAsCAkALAgJACwICQAsCAkALAgJACwICQAsDAOKRVdUFVfbSq3rOMgQBglSzjjPTlSU4t4XEAYOWMQlpVFyX5uSRvXM44ALBapmekf5DklUm+PR8FAFbPwiGtquckubW7r99ju+NVdbKqTm5tbS26OwA4lCZnpE9N8tyq+kKStyd5RlW99eyNuvtEd2929+bGxsZgdwBw+Cwc0u5+dXdf1N3HklyW5O+6+/lLmwwAVoD3kQLAwJFlPEh3fyjJh5bxWACwSpyRAsCAkALAgJACwICQAsCAkALAgJACwICQAsCAkALAgJACwICQAsCAkALAgJACwICQAsCAkALAgJACwICQAsCAkALAgJACwICQAsCAkALAgJACwICQAsCAkALAgJACwICQAsCAkALAwMIhraqHVNUHq+pUVd1cVS9f5mAAsAqODH72jiS/3t03VNV9k1xfVdd09yeXNBsAHHoLn5F29y3dfcP2199IcirJg5c1GACsgqX8jbSqjiV5fJLrlvF4ALAqxiGtqvskeWeSV3T313e4/3hVnayqk1tbW9PdAcChMgppVd0rZyL6tu6+eqdtuvtEd2929+bGxsZkdwBw6ExetVtJ3pTkVHe/YXkjAcDqmJyRPjXJC5I8o6pu3P549pLmAoCVsPDbX7r7H5LUEmcBgJXjykYAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAwCmlVXVJVn66qz1XVq5Y1FACsioVDWlUXJPnjJD+b5NFJLq+qRy9rMABYBZMz0icl+Vx3f767b0/y9iS
"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": "iVBORw0KGgoAAAANSUhEUgAAAdIAAAE/CAYAAADyukJqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAMlUlEQVR4nO3cX4yld13H8c/Xbonyx4DpqLVFFhJCgt60mRCQhBCQBKuxXnBREhCJyUYTFYyJQS+s3HlhiJoYzQZQjE25AKKEgEoQQky0cVqKtKwIokKl2kES/qgJNn692GPSbnd2D/M9O2eentcrmezMnGfn+c6vv+y7z5kzT3V3AIDj+bZtDwAASyakADAgpAAwIKQAMCCkADAgpAAwcOYkT3bDDTf02bNnT/KUADB27733frm79y732ImG9OzZszk4ODjJUwLAWFX9y1GPeWoXAAaEFAAGhBQABoQUAAaEFAAGhBQABoQUAAaEFAAGhBQABoQUAAaEFAAGhBQABk70pvVwGlRte4LH6972BMCEK1IAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABi4akir6p1V9UhVPfCYz31XVX24qj67+vNZ13ZMADid1rki/cMkr77kc29J8pHufn6Sj6w+BoCdc9WQdvfHk3zlkk/fnuRdq/ffleQnNjsWACzDcX9G+j3d/XCSrP787s2NBADLcc1fbFRV56rqoKoODg8Pr/XpAOBEHTek/15VNybJ6s9Hjjqwu89393537+/t7R3zdABwOh03pO9P8obV+29I8qebGQcAlmWdX3+5O8lfJ3lBVT1UVT+d5DeSvKqqPpvkVauPAWDnnLnaAd392iMeeuWGZwGAxXFnIwAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABg4s+0BYNfVW2vbIzxO39nbHgEWxRUpAAwIKQAMCCkADAgpAAyMQlpVv1hVD1bVA1V1d1V9+6YGA4AlOHZIq+qmJL+QZL+7fzDJdUnu2NRgALAE06d2zyT5jqo6k+SpSb40HwkAluPYIe3uf03ym0m+kOThJF/t7r/Y1GAAsASTp3afleT2JM9N8n1JnlZVr7vMceeq6qCqDg4PD48/KQCcQpOndn84yT9192F3/0+S9yX5oUsP6u7z3b3f3ft7e3uD0wHA6TMJ6ReSvLiqnlpVleSVSS5sZiwAWIbJz0jvSfKeJPcl+dTqa53f0FwAsAijm9Z3951J7tzQLACwOO5sBAADQgoAA0IKAANCCgADQgoAA0IKAANCCgADQgoAA0IKAANCCgADQgoAA0IKAANCCgADQgoAA0IKAANCCgADQgoAA0IKAANCCgADQgoAA0IKAANCCgADQgoAA0IKAANCCgADQgoAA0IKAANCCgADQgoAA0IKAANCCgADZ7Y9ANdI1bYneLzubU8AcE24IgWAASEFgAEhBYABIQWAASEFgAEhBYABIQWAASEFgAEhBYABIQWAgVFIq+qZVfWeqvr7qrpQVS/Z1GAAsATTe+3+dpI/6+7XVNVTkjx1AzMBwGIcO6RV9Z1JXpbkp5Kku7+Z5JubGQsAlmHy1O7zkhwm+YOq+kRVvb2qnnbpQVV1rqoOqurg8PBwcDoAOH0mIT2T5NYkv9fdtyT5zyRvufSg7j7f3fvdvb+3tzc4HQCcPpOQPpTkoe6+Z/Xxe3IxrADsoqrT83aCjh3S7v63JF+sqhesPvXKJJ/eyFQAsBDTV+3+fJK7Vq/Y/XySN85HAoDlGIW0u+9Psr+ZUQBgedzZCAAGhBQABoQUAAaEFAAGhBQABoQUAAaEFAAGhBQABqZ3NgKebE74PqVX1b3tCeCKXJECwICQAsCAkALAgJ+Rbki99XT9XMlPlQBOhitSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYWHRIq07PGwC7adEhBYBtE1IAGBBSABgQUgAYGIe0qq6rqk9U1Qc2MRAALMkmrkjflOTCBr4OACzOKKRVdXOSH03y9s2MAwDLMr0i/a0kv5zkf+ejAMDyHDukVfVjSR7p7nuvcty5qjqoqoPDw8Pjng4ATqXJFelLk/x4Vf1zkncneUVV/fGlB3X3+e7e7+79vb29wekA4PQ5dki7+1e6++buPpvkjiR/2d2v29hkALAAfo8UAAbObOKLdPfHknxsE18LAJbEFSkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAyc2fYAAEtRte0JHq972xOQuCIFgBEhBYABIQWAASEFgAEhBYABIQWAASEFgAEhBYABIQWAASEFgAEhBYABIQWAASEFgAEhBYABIQWAASEFgAEhBYABIQWAASEFgAEhBYABIQWAgWOHtKqeXVUfraoLVfVgVb1pk4MBwBKcGfzdR5P8UnffV1XPSHJvVX24uz+9odkA4NQ79hVpdz/c3fet3v96kgtJbtrUYACwBBv5GWlVnU1yS5J7LvPYuao6qKqDw8PDTZwOAE6NcUir6ulJ3pvkzd39tUsf7+7z3b3f3ft7e3vT0wHAqTIKaVVdn4sRvau737eZkQBgOY79YqOqqiTvSHKhu9+2uZEAWEe9tbY9wuP0tgfYkskV6UuTvD7JK6rq/tXbbRuaCwAW4dhXpN39V0lO1/8OAcAJc2cjABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYGIW0ql5dVZ+pqs9V1Vs2NRQALMWxQ1pV1yX53SQ/kuSFSV5bVS/c1GAAsASTK9IXJflcd3++u7+Z5N1Jbt/MWACwDJOQ3pTki4/5+KHV5wBgZ5wZ/N26zOf6CQdVnUtybvXhN6rqM4NzXgs3JPny+Kv8+vgrbNTl/uMc02bWpzY40elh71yZvXM0e+dop3XfPOeoByYhfSjJsx/z8c1JvnTpQd19Psn5wXmuqao66O79bc9xWlmfo1mbK7M+R7M2R1vi2kye2v3bJM+vqudW1VOS3JHk/ZsZCwCW4dhXpN39aFX9XJI/T3Jdknd294MbmwwAFmDy1G66+4NJPrihWbbl1D7tfEpYn6NZmyuzPkezNkdb3NpU9xNeHwQArMktAgFgYGdCerXbGdZFv7N6/O+q6tZtzLkNa6zNy6vqq1V1/+rt17Yx5zZU1Tur6pGqeuCIx3d23yRrrc8u751nV9VHq+pCVT1YVW+6zDE7uX/WXJvl7J3uftK/5eKLof4xyfOSPCXJJ5O88JJjbkvyoVz8VagXJ7ln23OforV5eZIPbHvWLa3Py5LcmuSBIx7fyX3zLazPLu+dG5Pcunr/GUn+wb8739LaLGbv7MoV6Tq3M7w9yR/1RX+T5JlVdeNJD7oFbvV4Bd398SRfucIhu7pvkqy1Pjurux/u7vtW7389yYU88e5vO7l/1lybxdiVkK5zO8NdveXhut/3S6rqk1X1oar6gZMZbRF2dd98K3Z+71TV2SS3JLnnkod2fv9cYW2Sheyd0a+/LMg6tzNc65aHT0LrfN/3JXlOd3+jqm5L8idJnn+tB1uIXd0369r5vVNVT0/y3iRv7u6vXfrwZf7Kzuy
"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": "iVBORw0KGgoAAAANSUhEUgAAAdIAAAE/CAYAAADyukJqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAOAUlEQVR4nO3db4hld33H8c+32UgbtWjJWK1JXS0SsFIwDKIVrJgKaSraBz5IQGutsLTQVkuL1QpGnxVa7B9aKoumWhriA7WtiLYGq4SChk5ioonrv1qrq2kzNuCfWrCh3z7YEZJ1JzOZ792dObmvFww7954zc7578iNvzp07Z6u7AwAczA8d9gAAsGRCCgADQgoAA0IKAANCCgADQgoAA8cu5MEuvfTSPn78+IU8JACM3Xbbbd/o7o1zbbugIT1+/Hi2trYu5CEBYKyq/n23bV7aBYABIQWAASEFgAEhBYABIQWAASEFgAEhBYABIQWAASEFgAEhBYCBPUNaVTdU1b1Vddc5tv1uVXVVXXp+xgOAo20/V6TvTHL12U9W1eVJXpTkKyueCQAWY8+QdvctSe47x6Y/TvK6JL3qoQBYoKqj83EBHehnpFX1kiRf6+4797Hviaraqqqt7e3tgxwOAI6shx3SqrokyRuTvGk/+3f3ye7e7O7NjY1z/lNuALBYB7ki/akkT01yZ1V9OcllSW6vqieucjAAWIKH/Q97d/enkzzh+493YrrZ3d9Y4VwAsAj7+fWXm5J8PMkVVXW6ql59/scCgGXY84q0u6/bY/vxlU0DAAvjzkYAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwsGdIq+qGqrq3qu56wHN/WFWfrapPVdXfVtXjzuuUAHBE7eeK9J1Jrj7ruZuTPLO7fybJ55O8YcVzAcAi7BnS7r4lyX1nPffh7r5/5+Enklx2HmYDgCNvFT8j/dUkH9ptY1WdqKqtqtra3t5eweEA4OgYhbSq3pjk/iQ37rZPd5/s7s3u3tzY2JgcDgCOnGMH/cKqemWSFye5qrt7dSMBwHIcKKRVdXWS30vyc9393dWOBADLsZ9ff7kpyceTXFFVp6vq1Un+PMljk9xcVXdU1dvO85wAcCTteUXa3ded4+l3nIdZAGBx3NkIAAaEFAAGhBQABoQUAAaEFAAGhBQABoQUAAaEFAAGhBQABoQUAAaEFAAGhBQABoQUAAaEFAAGhBQABoQUAAaEFAAGhBQABoQUAAaEFAAGhBQABoQUAAaEFAAGhBQABoQUAAaEFAAGhBQABoQUAAaEFAAGhBQABvYMaVXdUFX3VtVdD3jux6rq5qr6ws6fjz+/YwLA0bSfK9J3Jrn6rOden+Qj3f30JB/ZeQwAa2fPkHb3LUnuO+vplyZ5187n70ryS6sdCwCW4aA/I/3x7r4nSXb+fMJuO1bViaraqqqt7e3tAx4OAI6m8/5mo+4+2d2b3b25sbFxvg8HABfUQUP6n1X1pCTZ+fPe1Y0EAMtx0JC+P8krdz5/ZZK/X804ALAs+/n1l5uSfDzJFVV1uqpeneQPkryoqr6Q5EU7jwFg7Rzba4fuvm6XTVeteBYAWBx3NgKAASEFgAEhBYABIQWAASEFgAEhBYABIQWAASEFgAEhBYABIQWAASEFgAEhBYABIQWAASEFgAEhBYABIQWAASEFgAEhBYCBY4c9AMBSVB32BA/WfdgTkLgiBYARIQWAASEFgAEhBYABIQWAASEFgAEhBYABIQWAASEFgAEhBYCBUUir6rer6u6ququqbqqqH17VYACwBAcOaVU9OclvJdns7mcmuSjJtasaDACWYPrS7rEkP1JVx5JckuTr85EAYDkOHNLu/lqSP0rylST3JPlmd394VYMBwBJMXtp9fJKXJnlqkp9I8uiqevk59jtRVVtVtbW9vX3wSQHgCJq8tPvzSf6tu7e7+3+TvC/Jz569U3ef7O7N7t7c2NgYHA4Ajp5JSL+S5DlVdUlVVZKrkpxazVgAsAyTn5HemuQ9SW5P8umd73VyRXMBwCIcm3xxd1+f5PoVzQIAi+PORgAwMLoiBeDw1FvqsEd4kD7sAQ6JK1IAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBiFtKoeV1XvqarPVtWpqnruqgYDgCU4Nvz6P03yD939sqp6VJJLVjATACzGgUNaVT+a5PlJfiVJuvt7Sb63mrEAYBkmL+0+Lcl2kr+qqk9W1dur6tErmgsAFmES0mNJrkzyl939rCT/neT1Z+9UVSeqaquqtra3tweHA4CjZxLS00lOd/etO4/fkzNhfZDuPtndm929ubGxMTgcABw9Bw5pd/9Hkq9W1RU7T12V5DMrmQoAFmL6rt3fTHLjzjt2v5TkVfORAGA5RiHt7juSbK5mFABYHnc2AoABIQWAASEFgAEhBYABIQWAASEFgAEhBYABIQWAASEFgAEhBYABIQWAASEFgAEhBYCB6T+jBgzVW+qwR3iQvr4PewRYFFekADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMODORsCD1dG601LanZY42oR0RdzmDWA9eWkXAAYWfUV6pF6BevNhD3CWI3Vy4uU54BHLFSkADAgpAAwIKQAMjENaVRdV1Ser6gOrGAgAlmQVV6SvSXJqBd8HABZnFNKquizJLyZ5+2rGAYBlmV6R/kmS1yX5v/koALA8Bw5pVb04yb3dfdse+52oqq2q2tre3j7o4QDgSJpckT4vyUuq6stJ3p3khVX1N2fv1N0nu3uzuzc3NjYGhwOAo+fAIe3uN3T3Zd19PMm1Sf6pu1++sskAYAH8HikADKzkXrvd/bEkH1vF9wKAJXFFCgADQgoAA0IKAANCCgADQgoAA0IKAANCCgADQgoAA0IKAANCCgADQgoAA0LK2qk6Wh/AsgkpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADAgpAAwIKQAMCCkADBw4pFV1eVV9tKpOVdXdVfWaVQ4GAEtwbPC19yf5ne6+vaoem+S2qrq5uz+zotkA4Mg78BVpd9/T3bfvfP7tJKeSPHlVgwHAEqzkZ6RVdTzJs5LcuorvBwBLMQ5pVT0myXuTvLa7v3WO7Seqaquqtra3t6eHA4AjZRTSqro4ZyJ6Y3e/71z7dPfJ7t7s7s2NjY3J4QDgyJm8a7eSvCPJqe5+6+pGAoDlmFyRPi/JK5K8sKru2Pm4ZkVzAcAiHPjXX7r7n5PUCmcBgMVxZyMAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYEFIAGBBSABgQUgAYGIW0qq6uqs9V1Rer6vWrGgoAluLAIa2qi5L8RZJfSPKMJNdV1TNWNRgALMHkivTZSb7Y3V/q7u8leXeSl65mLABYhklIn5zkqw94fHrnOQBYG8cGX1vneK5/YKeqE0lO7Dz8TlV9bnDM8+HSJN8Yf5c3j7/DSp3rP84Breb81AonOjqsnYdm7ezO2tndUV03T9ltwySkp5Nc/oDHlyX5+tk7dffJJCcHxzmvqmqruzcPe46jyvnZnXPz0Jyf3Tk3u1viuZm8tPsvSZ5eVU+tqkcluTbJ+1czFgAsw4GvSLv7/qr6jST/mOSiJDd0990rmwwAFmDy0m66+4NJPriiWQ7LkX3Z+Yhwfnbn3Dw052d3zs3uFnduqvsH3h8EAOy
"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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
"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,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
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"b=0\n",
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" employee group Address\n",
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"b=0\n",
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"metadata": {
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{
"name": "stdout",
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"text": [
"A\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",
"0 Bob Accounting 2\n",
"1 Jake Engineering 7\n",
"2 Lisa Engineering 12\n",
"3 Sue HR 17\n",
"A\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",
"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\n"
]
},
{
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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": {},
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}
],
"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,
"jupyter": {
"outputs_hidden": true
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"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": []
},
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"cell_type": "code",
"execution_count": null,
"metadata": {},
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},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
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" <th>secnd</th>\n",
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" <td>877.50</td>\n",
" <td>2.60</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\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>290.00</td>\n",
" <td>7350.0</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",
" </tr>\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>290.00</td>\n",
" <td>7350.0</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",
" </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>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>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",
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"\n",
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" }\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",
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" <td>1</td>\n",
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" <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",
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" <td>...</td>\n",
" <td>...</td>\n",
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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",
" </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"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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" <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",
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" <td>1</td>\n",
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" <td>320.0</td>\n",
" <td>320.0</td>\n",
" <td>6750.0</td>\n",
" <td>...</td>\n",
" <td>877.50</td>\n",
" <td>2.60</td>\n",
" <td>3</td>\n",
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" <th>259340</th>\n",
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" <td>...</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>NaN</td>\n",
" <td>3.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>144</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>2128.44</td>\n",
" <td>6.31</td>\n",
" <td>261357</td>\n",
" <td>118</td>\n",
" <td>1798</td>\n",
" <td>NaN</td>\n",
" <td>3.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>144</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>2128.44</td>\n",
" <td>6.31</td>\n",
" <td>261358</td>\n",
" <td>119</td>\n",
" <td>1799</td>\n",
" <td>NaN</td>\n",
" <td>3.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>144</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>2128.44</td>\n",
" <td>6.31</td>\n",
" <td>261359</td>\n",
" <td>120</td>\n",
" <td>1800</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>144</td>\n",
" </tr>\n",
" </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": {
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"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"
]
},
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"source": [
"l3 = list(range(120,1800,120))"
]
},
{
"cell_type": "code",
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"metadata": {},
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{
"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"
]
},
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{
"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"
}
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{
"data": {
"image/png": "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
"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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
"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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
"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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
"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": [
"<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>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",
" <th>...</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",
" <th>choc1</th>\n",
" <th>choc2</th>\n",
" <th>newid</th>\n",
" <th>choc1_nn</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>7204</th>\n",
" <td>170703_0850</td>\n",
" <td>1</td>\n",
" <td>HH</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>350.0</td>\n",
" <td>300.0</td>\n",
" <td>250.0</td>\n",
" <td>6750.0</td>\n",
" <td>...</td>\n",
" <td>1.27</td>\n",
" <td>484</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7205</th>\n",
" <td>170703_0850</td>\n",
" <td>1</td>\n",
" <td>HH</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>350.0</td>\n",
" <td>300.0</td>\n",
" <td>250.0</td>\n",
" <td>6750.0</td>\n",
" <td>...</td>\n",
" <td>1.27</td>\n",
" <td>485</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td></td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <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",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>350.0</td>\n",
" <td>300.0</td>\n",
" <td>250.0</td>\n",
" <td>6750.0</td>\n",
" <td>...</td>\n",
" <td>1.27</td>\n",
" <td>486</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td></td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7207</th>\n",
" <td>170703_0850</td>\n",
" <td>1</td>\n",
" <td>HH</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>350.0</td>\n",
" <td>300.0</td>\n",
" <td>250.0</td>\n",
" <td>6750.0</td>\n",
" <td>...</td>\n",
" <td>1.27</td>\n",
" <td>487</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td></td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7208</th>\n",
" <td>170703_0850</td>\n",
" <td>1</td>\n",
" <td>HH</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>350.0</td>\n",
" <td>300.0</td>\n",
" <td>250.0</td>\n",
" <td>6750.0</td>\n",
" <td>...</td>\n",
" <td>1.27</td>\n",
" <td>488</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td></td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>0</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>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",
" <td>10369.9</td>\n",
" <td>...</td>\n",
" <td>-1.89</td>\n",
" <td>14152</td>\n",
" <td>116</td>\n",
" <td>1796</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>9001</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>14153</td>\n",
" <td>117</td>\n",
" <td>1797</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>9002</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>14154</td>\n",
" <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",
" <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>14156</td>\n",
" <td>120</td>\n",
" <td>1800</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",
" </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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
"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",
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" <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
}
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