diff --git a/0_Cleaning_and_merge.ipynb b/0_Cleaning_and_merge.ipynb index 3f3b639..a3018ba 100644 --- a/0_Cleaning_and_merge.ipynb +++ b/0_Cleaning_and_merge.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 28, "id": "15103481-8d74-404c-aa09-7601fe7730da", "metadata": {}, "outputs": [], @@ -119,19 +119,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "dd6a3518-b752-4a1e-b77b-9e03e853c3ed", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_50143/4081512283.py:10: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " df = pd.read_csv(file_in)\n" - ] - } - ], + "outputs": [], "source": [ "# loop to create dataframes from liste\n", "files_path = liste_database\n", @@ -158,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "d237be96-8c86-4a91-b7a1-487e87a16c3d", "metadata": {}, "outputs": [], @@ -215,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "b95464b1-26bc-4aac-84b4-45da83b92251", "metadata": {}, "outputs": [], @@ -258,218 +249,20 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "3e1d2ba7-ff4f-48eb-93a8-2bb648c70396", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_50143/1320335767.py:5: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " tickets.rename(columns = {'id' : 'ticket_id'}, inplace = True)\n", - "/tmp/ipykernel_50143/1320335767.py:9: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " suppliers.rename(columns = {'name' : 'supplier_name'}, inplace = True)\n" - ] - } - ], + "outputs": [], "source": [ "df1_ticket_information = preprocessing_tickets_area(tickets = df1_tickets, purchases = df1_purchases, suppliers = df1_suppliers, type_ofs = df1_type_ofs)" ] }, { "cell_type": "code", - "execution_count": 70, + "execution_count": null, "id": "4b18edfc-6450-4c6a-9e7b-ee5a5808c8c9", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ticket_idproduct_idis_from_subscriptiontype_ofsupplier_namepurchase_datecustomer_id
013070859225251False1vente en ligne2018-12-28 14:47:50+00:0048187
113070860224914False1vente en ligne2018-12-28 14:47:50+00:0048187
213070861224914False1vente en ligne2018-12-28 14:47:50+00:0048187
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413070863224914False1vente en ligne2018-12-28 14:47:50+00:0048187
........................
182666720662815405689False1vente en ligne2023-11-08 17:23:54+00:001256135
182666820662816403658False1vente en ligne2023-11-08 18:32:18+00:001256136
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182667020662818403658False1vente en ligne2023-11-08 19:30:28+00:001256137
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idcustomer_idopened_atsent_atdelivered_atcampaign_namecampaign_service_idcampaign_sent_at
019793112597NaT2021-03-28 16:01:09+00:002021-03-28 16:24:18+00:00Le Mucem chez vous, gardons le lien #224042021-03-27 23:00:00+00:00
114211113666NaT2021-03-28 16:01:09+00:002021-03-28 16:21:02+00:00Le Mucem chez vous, gardons le lien #224042021-03-27 23:00:00+00:00
213150280561NaT2021-03-28 16:00:59+00:002021-03-28 16:08:45+00:00Le Mucem chez vous, gardons le lien #224042021-03-27 23:00:00+00:00
370731010072021-03-28 18:11:06+00:002021-03-28 16:00:59+00:002021-03-28 16:09:47+00:00Le Mucem chez vous, gardons le lien #224042021-03-27 23:00:00+00:00
45175103972NaT2021-03-28 16:01:06+00:002021-03-28 16:05:03+00:00Le Mucem chez vous, gardons le lien #224042021-03-27 23:00:00+00:00
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This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n", - "\n", - "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n", - "\n", - "\n", - " campaigns_reduced['nb_campaigns_opened'].fillna(0, inplace=True)\n" - ] - } - ], + "outputs": [], "source": [ "df1_campaigns_kpi = campaigns_kpi(campaigns_information = df1_campaigns_information) " ] }, { "cell_type": "code", - "execution_count": 66, + "execution_count": null, "id": "6be2a9a6-056b-4e19-8c26-a18ba3df36b3", "metadata": {}, + "outputs": [], + "source": [ + "df1_campaigns_kpi" + ] + }, + { + "cell_type": "markdown", + "id": "56520a97-ede8-4920-a211-3b5b136af33d", + "metadata": {}, + "source": [ + "## Create Products Table" + ] + }, + { + "cell_type": "markdown", + "id": "9782e9d3-ba20-46bf-8562-bd0969972ddc", + "metadata": {}, + "source": [ + "Some useful functions" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "30488a40-1b38-4b9a-9d3b-26a0597c5e6d", + "metadata": {}, + "outputs": [], + "source": [ + "BUCKET = \"bdc2324-data\"\n", + "directory_path = '1'" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "607eb4b4-eed9-4b50-b823-f75c116dd37c", + "metadata": {}, + "outputs": [], + "source": [ + "def display_databases(file_name):\n", + " \"\"\"\n", + " This function returns the file from s3 storage\n", + " \"\"\"\n", + " file_path = BUCKET + \"/\" + directory_path + \"/\" + file_name\n", + " print(\"File path : \", file_path)\n", + " with fs.open(file_path, mode=\"rb\") as file_in:\n", + " df = pd.read_csv(file_in, sep=\",\")\n", + " \n", + " print(\"Shape : \", df.shape)\n", + " return df\n", + "\n", + "\n", + "def remove_horodates(df):\n", + " \"\"\"\n", + " this function remove horodate columns like created_at and updated_at\n", + " \"\"\"\n", + " df = df.drop(columns = [\"created_at\", \"updated_at\"])\n", + " return df\n", + "\n", + "\n", + "def order_columns_id(df):\n", + " \"\"\"\n", + " this function puts all id columns at the beginning in order to read the dataset easier\n", + " \"\"\"\n", + " substring = 'id'\n", + " id_columns = [col for col in df.columns if substring in col]\n", + " remaining_col = [col for col in df.columns if substring not in col]\n", + " new_order = id_columns + remaining_col\n", + " return df[new_order]\n", + "\n", + "\n", + "def process_df_2(df):\n", + " \"\"\"\n", + " This function organizes dataframe\n", + " \"\"\"\n", + " df = remove_horodates(df)\n", + " print(\"Number of columns : \", len(df.columns))\n", + " df = order_columns_id(df)\n", + " print(\"Columns : \", df.columns)\n", + " return df\n", + "\n", + "def load_dataset(name):\n", + " \"\"\"\n", + " This function loads csv file\n", + " \"\"\"\n", + " df = display_databases(name)\n", + " df = process_df_2(df)\n", + " # drop na :\n", + " #df = df.dropna(axis=1, thresh=len(df))\n", + " # if identifier in table : delete it\n", + " if 'identifier' in df.columns:\n", + " df = df.drop(columns = 'identifier')\n", + " return df" + ] + }, + { + "cell_type": "markdown", + "id": "d23f28c0-bc95-438b-8d14-5b7bb6e267bd", + "metadata": {}, + "source": [ + "Create theme tables" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "350b09b9-451f-4d47-81fe-f34b892db027", + "metadata": {}, + "outputs": [], + "source": [ + "def create_products_table():\n", + " # first merge products and categories\n", + " print(\"first merge products and categories\")\n", + " products = load_dataset(\"1products.csv\")\n", + " categories = load_dataset(\"1categories.csv\")\n", + " # Drop useless columns\n", + " products = products.drop(columns = ['apply_price', 'extra_field', 'amount_consumption'])\n", + " categories = categories.drop(columns = ['extra_field', 'quota'])\n", + "\n", + " #Merge\n", + " products_theme = products.merge(categories, how = 'left', left_on = 'category_id',\n", + " right_on = 'id', suffixes=('_products', '_categories'))\n", + " products_theme = products_theme.rename(columns = {\"name\" : \"name_categories\"})\n", + " \n", + " # Second merge products_theme and type of categories\n", + " print(\"Second merge products_theme and type of categories\")\n", + " type_of_categories = load_dataset(\"1type_of_categories.csv\")\n", + " type_of_categories = type_of_categories.drop(columns = 'id')\n", + " products_theme = products_theme.merge(type_of_categories, how = 'left', left_on = 'category_id',\n", + " right_on = 'category_id' )\n", + "\n", + " # Index cleaning\n", + " products_theme = products_theme.drop(columns = ['id_categories'])\n", + " products_theme = order_columns_id(products_theme)\n", + " return products_theme\n", + "\n", + "\n", + "def create_events_table():\n", + " # first merge events and seasons : \n", + " print(\"first merge events and seasons : \")\n", + " events = load_dataset(\"1events.csv\")\n", + " seasons = load_dataset(\"1seasons.csv\")\n", + "\n", + " # Drop useless columns\n", + " events = events.drop(columns = ['manual_added', 'is_display'])\n", + " seasons = seasons.drop(columns = ['start_date_time'])\n", + " \n", + " events_theme = events.merge(seasons, how = 'left', left_on = 'season_id', right_on = 'id', suffixes=('_events', '_seasons'))\n", + "\n", + " # Secondly merge events_theme and event_types\n", + " print(\"Secondly merge events_theme and event_types : \")\n", + " event_types = load_dataset(\"1event_types.csv\")\n", + " event_types = event_types.drop(columns = ['fidelity_delay'])\n", + " \n", + " events_theme = events_theme.merge(event_types, how = 'left', left_on = 'event_type_id', right_on = 'id', suffixes=('_events', '_event_type'))\n", + " events_theme = events_theme.rename(columns = {\"name\" : \"name_event_types\"})\n", + " events_theme = events_theme.drop(columns = 'id')\n", + "\n", + " # thirdly merge events_theme and facilities\n", + " print(\"thirdly merge events_theme and facilities : \")\n", + " facilities = load_dataset(\"1facilities.csv\")\n", + " facilities = facilities.drop(columns = ['fixed_capacity'])\n", + " \n", + " events_theme = events_theme.merge(facilities, how = 'left', left_on = 'facility_id', right_on = 'id', suffixes=('_events', '_facility'))\n", + " events_theme = events_theme.rename(columns = {\"name\" : \"name_facilities\", \"id_events\" : \"event_id\"})\n", + " events_theme = events_theme.drop(columns = 'id')\n", + "\n", + " # Index cleaning\n", + " events_theme = events_theme.drop(columns = ['id_seasons'])\n", + " events_theme = order_columns_id(events_theme)\n", + " return events_theme\n", + "\n", + "\n", + "def create_representations_table():\n", + " representations = load_dataset(\"1representations.csv\")\n", + " representations = representations.drop(columns = ['serial', 'open', 'satisfaction', 'is_display', 'expected_filling',\n", + " 'max_filling', 'extra_field', 'start_date_time', 'end_date_time', 'name',\n", + " 'representation_type_id'])\n", + " \n", + " representations_capacity = load_dataset(\"1representation_category_capacities.csv\")\n", + " representations_capacity = representations_capacity.drop(columns = ['expected_filling', 'max_filling'])\n", + "\n", + " representations_theme = representations.merge(representations_capacity, how='left',\n", + " left_on='id', right_on='representation_id',\n", + " suffixes=('_representation', '_representation_cap'))\n", + " # index cleaning\n", + " representations_theme = representations_theme.drop(columns = [\"id_representation\"])\n", + " representations_theme = order_columns_id(representations_theme)\n", + " return representations_theme" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "0fccc8ef-e575-4857-a401-94a7274394df", + "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "first merge products and categories\n", + "File path : bdc2324-data/1/1products.csv\n", + "Shape : (94803, 14)\n", + "Number of columns : 12\n", + "Columns : Index(['id', 'representation_id', 'pricing_formula_id', 'category_id',\n", + " 'products_group_id', 'product_pack_id', 'identifier', 'amount',\n", + " 'is_full_price', 'apply_price', 'extra_field', 'amount_consumption'],\n", + " dtype='object')\n", + "File path : bdc2324-data/1/1categories.csv\n", + "Shape : (27, 7)\n", + "Number of columns : 5\n", + "Columns : Index(['id', 'identifier', 'name', 'extra_field', 'quota'], dtype='object')\n", + "Second merge products_theme and type of categories\n", + "File path : bdc2324-data/1/1type_of_categories.csv\n", + "Shape : (5, 6)\n", + "Number of columns : 4\n", + "Columns : Index(['id', 'type_of_id', 'category_id', 'identifier'], dtype='object')\n" + ] + }, { "data": { "text/html": [ @@ -1312,133 +680,673 @@ " \n", " \n", " \n", - " customer_id\n", - " nb_campaigns\n", - " nb_campaigns_opened\n", - " time_to_open\n", + " id_products\n", + " representation_id\n", + " pricing_formula_id\n", + " category_id\n", + " products_group_id\n", + " product_pack_id\n", + " type_of_id\n", + " amount\n", + " is_full_price\n", + " name_categories\n", " \n", " \n", " \n", " \n", " 0\n", - " 2\n", - " 4\n", - " 0.0\n", - " NaT\n", + " 10682\n", + " 914\n", + " 114\n", + " 41\n", + " 10655\n", + " 1\n", + " NaN\n", + " 9.0\n", + " False\n", + " indiv activité tr\n", " \n", " \n", " 1\n", - " 3\n", - " 222\n", - " 124.0\n", - " 1 days 00:28:30.169354838\n", + " 478\n", + " 273\n", + " 131\n", + " 1\n", + " 471\n", + " 1\n", + " 12.0\n", + " 9.5\n", + " False\n", + " indiv entrées tp\n", " \n", " \n", " 2\n", - " 4\n", - " 7\n", - " 7.0\n", - " 1 days 04:31:01.428571428\n", + " 20873\n", + " 275\n", + " 137\n", + " 1\n", + " 20825\n", + " 1\n", + " 12.0\n", + " 11.5\n", + " False\n", + " indiv entrées tp\n", " \n", " \n", " 3\n", + " 157142\n", + " 82519\n", + " 9\n", " 5\n", - " 4\n", - " 0.0\n", - " NaT\n", + " 156773\n", + " 1\n", + " NaN\n", + " 8.0\n", + " False\n", + " indiv entrées tr\n", " \n", " \n", " 4\n", - " 6\n", - " 20\n", - " 0.0\n", - " NaT\n", - " \n", - " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " \n", - " \n", - " 130467\n", - " 1256097\n", + " 1341\n", + " 9\n", + " 93\n", " 1\n", - " 1.0\n", - " 0 days 02:11:15\n", - " \n", - " \n", - " 130468\n", - " 1256098\n", + " 1175\n", " 1\n", - " 0.0\n", - " NaT\n", - " \n", - " \n", - " 130469\n", - " 1256099\n", - " 1\n", - " 0.0\n", - " NaT\n", - " \n", - " \n", - " 130470\n", - " 1256100\n", - " 1\n", - " 0.0\n", - " NaT\n", - " \n", - " \n", - " 130471\n", - " 1256101\n", - " 1\n", - " 0.0\n", - " NaT\n", + " 12.0\n", + " 8.5\n", + " False\n", + " indiv entrées tp\n", " \n", " \n", "\n", - "

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" + ], + "text/plain": [ + " event_id id_representation_cap representation_id category_id\n", + "0 12384 123058 84820 2\n", + "1 37 2514 269 2\n", + "2 37 384 269 5\n", + "3 37 2515 269 10\n", + "4 37 383 269 1" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "representation_theme = create_representations_table()\n", + "representation_theme.head()" + ] + }, + { + "cell_type": "markdown", + "id": "8fa191d5-c867-4d4d-bbab-f29d7d91ce6a", + "metadata": {}, + "source": [ + "Create uniform product database " + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "15a62ed6-35e4-4abc-aeef-a7daeec0a4ba", + "metadata": {}, + "outputs": [], + "source": [ + "def uniform_product_df():\n", + " \"\"\"\n", + " This function returns the uniform product dataset\n", + " \"\"\"\n", + " print(\"Products theme columns : \", products_theme.columns)\n", + " print(\"\\n Representation theme columns : \", representation_theme.columns)\n", + " print(\"\\n Events theme columns : \", events_theme.columns)\n", + "\n", + " products_global = products_theme.merge(representation_theme, how='left',\n", + " on= [\"representation_id\", \"category_id\"])\n", + " \n", + " products_global = products_global.merge(events_theme, how='left', on='event_id',\n", + " suffixes = (\"_representation\", \"_event\"))\n", + " \n", + " products_global = order_columns_id(products_global)\n", + "\n", + " # remove useless columns \n", + " products_global = products_global.drop(columns = ['type_of_id', 'name_events', 'name_seasons', 'name_categories'])\n", + " return products_global" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "89dc9685-1de9-4ce3-a6c0-8d7f1931a951", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Products theme columns : Index(['id_products', 'representation_id', 'pricing_formula_id', 'category_id',\n", + " 'products_group_id', 'product_pack_id', 'type_of_id', 'amount',\n", + " 'is_full_price', 'name_categories'],\n", + " dtype='object')\n", + "\n", + " Representation theme columns : Index(['event_id', 'id_representation_cap', 'representation_id',\n", + " 'category_id'],\n", + " dtype='object')\n", + "\n", + " Events theme columns : Index(['event_id', 'season_id', 'facility_id', 'event_type_id',\n", + " 'event_type_key_id', 'facility_key_id', 'street_id', 'name_events',\n", + " 'name_seasons', 'name_event_types', 'name_facilities'],\n", + " dtype='object')\n" + ] + }, + { + "data": { + "text/html": [ + "
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id_productsrepresentation_idpricing_formula_idcategory_idproducts_group_idproduct_pack_idevent_idid_representation_capseason_idfacility_idevent_type_idevent_type_key_idfacility_key_idstreet_idamountis_full_pricename_event_typesname_facilities
0106829141144110655113287894125119.0Falseoffre muséale individuelmucem
147827313114711373902122119.5Falseoffre muséale individuelmucem
22087327513712082513739521221111.5Falseoffre muséale individuelmucem
315714282519951567731123651201991754124118.0Falseoffre muséale individuelmucem
413419931117518214136118.5Falsenon définimucem
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" + ], + "text/plain": [ + " id_products representation_id pricing_formula_id category_id \\\n", + "0 10682 914 114 41 \n", + "1 478 273 131 1 \n", + "2 20873 275 137 1 \n", + "3 157142 82519 9 5 \n", + "4 1341 9 93 1 \n", + "\n", + " products_group_id product_pack_id event_id id_representation_cap \\\n", + "0 10655 1 132 8789 \n", + "1 471 1 37 390 \n", + "2 20825 1 37 395 \n", + "3 156773 1 12365 120199 \n", + "4 1175 1 8 21 \n", + "\n", + " season_id facility_id event_type_id event_type_key_id facility_key_id \\\n", + "0 4 1 2 5 1 \n", + "1 2 1 2 2 1 \n", + "2 2 1 2 2 1 \n", + "3 1754 1 2 4 1 \n", + "4 4 1 3 6 1 \n", + "\n", + " street_id amount is_full_price name_event_types name_facilities \n", + "0 1 9.0 False offre muséale individuel mucem \n", + "1 1 9.5 False offre muséale individuel mucem \n", + "2 1 11.5 False offre muséale individuel mucem \n", + "3 1 8.0 False offre muséale individuel mucem \n", + "4 1 8.5 False non défini mucem " + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "products_global = uniform_product_df()\n", + "products_global.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "117d172a-2195-4060-9245-96c6f637ebbd", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": {