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Atividade semana 14 - Carol #26

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3,381 changes: 3,381 additions & 0 deletions exercicios/para-casa/ativ-carol/atividade_semana_14.ipynb
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48,536 changes: 48,536 additions & 0 deletions exercicios/para-casa/ativ-carol/base_atividade_s14_olist.csv
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103,887 changes: 103,887 additions & 0 deletions exercicios/para-casa/ativ-carol/datasets_olist/olist_order_payments_dataset.csv
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104,720 changes: 104,720 additions & 0 deletions exercicios/para-casa/ativ-carol/datasets_olist/olist_order_reviews_dataset.csv
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99,442 changes: 99,442 additions & 0 deletions exercicios/para-casa/ativ-carol/datasets_olist/olist_orders_dataset.csv
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395 changes: 395 additions & 0 deletions exercicios/para-sala/dinamica.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# importando os pacotes que iremos utilizar\n",
"import pandas as pd"
]
},
{
"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>User ID</th>\n",
" <th>Subscription Type</th>\n",
" <th>Monthly Revenue</th>\n",
" <th>Join Date</th>\n",
" <th>Last Payment Date</th>\n",
" <th>Country</th>\n",
" <th>Age</th>\n",
" <th>Gender</th>\n",
" <th>Device</th>\n",
" <th>Plan Duration</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Basic</td>\n",
" <td>10</td>\n",
" <td>15-01-22</td>\n",
" <td>10-06-23</td>\n",
" <td>United States</td>\n",
" <td>28</td>\n",
" <td>Male</td>\n",
" <td>Smartphone</td>\n",
" <td>1 Month</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>Premium</td>\n",
" <td>15</td>\n",
" <td>05-09-21</td>\n",
" <td>22-06-23</td>\n",
" <td>Canada</td>\n",
" <td>35</td>\n",
" <td>Female</td>\n",
" <td>Tablet</td>\n",
" <td>1 Month</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>Standard</td>\n",
" <td>12</td>\n",
" <td>28-02-23</td>\n",
" <td>27-06-23</td>\n",
" <td>United Kingdom</td>\n",
" <td>42</td>\n",
" <td>Male</td>\n",
" <td>Smart TV</td>\n",
" <td>1 Month</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>Standard</td>\n",
" <td>12</td>\n",
" <td>10-07-22</td>\n",
" <td>26-06-23</td>\n",
" <td>Australia</td>\n",
" <td>51</td>\n",
" <td>Female</td>\n",
" <td>Laptop</td>\n",
" <td>1 Month</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>Basic</td>\n",
" <td>10</td>\n",
" <td>01-05-23</td>\n",
" <td>28-06-23</td>\n",
" <td>Germany</td>\n",
" <td>33</td>\n",
" <td>Male</td>\n",
" <td>Smartphone</td>\n",
" <td>1 Month</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",
" </tr>\n",
" <tr>\n",
" <th>2495</th>\n",
" <td>2496</td>\n",
" <td>Premium</td>\n",
" <td>14</td>\n",
" <td>25-07-22</td>\n",
" <td>12-07-23</td>\n",
" <td>Spain</td>\n",
" <td>28</td>\n",
" <td>Female</td>\n",
" <td>Smart TV</td>\n",
" <td>1 Month</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2496</th>\n",
" <td>2497</td>\n",
" <td>Basic</td>\n",
" <td>15</td>\n",
" <td>04-08-22</td>\n",
" <td>14-07-23</td>\n",
" <td>Spain</td>\n",
" <td>33</td>\n",
" <td>Female</td>\n",
" <td>Smart TV</td>\n",
" <td>1 Month</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2497</th>\n",
" <td>2498</td>\n",
" <td>Standard</td>\n",
" <td>12</td>\n",
" <td>09-08-22</td>\n",
" <td>15-07-23</td>\n",
" <td>United States</td>\n",
" <td>38</td>\n",
" <td>Male</td>\n",
" <td>Laptop</td>\n",
" <td>1 Month</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2498</th>\n",
" <td>2499</td>\n",
" <td>Standard</td>\n",
" <td>13</td>\n",
" <td>12-08-22</td>\n",
" <td>12-07-23</td>\n",
" <td>Canada</td>\n",
" <td>48</td>\n",
" <td>Female</td>\n",
" <td>Tablet</td>\n",
" <td>1 Month</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2499</th>\n",
" <td>2500</td>\n",
" <td>Basic</td>\n",
" <td>15</td>\n",
" <td>13-08-22</td>\n",
" <td>12-07-23</td>\n",
" <td>United States</td>\n",
" <td>35</td>\n",
" <td>Female</td>\n",
" <td>Smart TV</td>\n",
" <td>1 Month</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2500 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" User ID Subscription Type Monthly Revenue Join Date Last Payment Date \\\n",
"0 1 Basic 10 15-01-22 10-06-23 \n",
"1 2 Premium 15 05-09-21 22-06-23 \n",
"2 3 Standard 12 28-02-23 27-06-23 \n",
"3 4 Standard 12 10-07-22 26-06-23 \n",
"4 5 Basic 10 01-05-23 28-06-23 \n",
"... ... ... ... ... ... \n",
"2495 2496 Premium 14 25-07-22 12-07-23 \n",
"2496 2497 Basic 15 04-08-22 14-07-23 \n",
"2497 2498 Standard 12 09-08-22 15-07-23 \n",
"2498 2499 Standard 13 12-08-22 12-07-23 \n",
"2499 2500 Basic 15 13-08-22 12-07-23 \n",
"\n",
" Country Age Gender Device Plan Duration \n",
"0 United States 28 Male Smartphone 1 Month \n",
"1 Canada 35 Female Tablet 1 Month \n",
"2 United Kingdom 42 Male Smart TV 1 Month \n",
"3 Australia 51 Female Laptop 1 Month \n",
"4 Germany 33 Male Smartphone 1 Month \n",
"... ... ... ... ... ... \n",
"2495 Spain 28 Female Smart TV 1 Month \n",
"2496 Spain 33 Female Smart TV 1 Month \n",
"2497 United States 38 Male Laptop 1 Month \n",
"2498 Canada 48 Female Tablet 1 Month \n",
"2499 United States 35 Female Smart TV 1 Month \n",
"\n",
"[2500 rows x 10 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_netflix = pd.read_csv('netflix.csv')\n",
"df_netflix"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Valores nulos por coluna do dataframe:\n"
]
},
{
"data": {
"text/plain": [
"User ID 0\n",
"Subscription Type 0\n",
"Monthly Revenue 0\n",
"Join Date 0\n",
"Last Payment Date 0\n",
"Country 0\n",
"Age 0\n",
"Gender 0\n",
"Device 0\n",
"Plan Duration 0\n",
"dtype: int64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(\"Valores nulos por coluna do dataframe:\")\n",
"df_netflix.isnull().sum() "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 Male\n",
"1 Female\n",
"2 Male\n",
"3 Female\n",
"4 Male\n",
" ... \n",
"2495 Female\n",
"2496 Female\n",
"2497 Male\n",
"2498 Female\n",
"2499 Female\n",
"Name: Gender, Length: 2500, dtype: object"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_netflix['Gender']"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 28\n",
"1 35\n",
"2 42\n",
"3 51\n",
"4 33\n",
" ..\n",
"2495 28\n",
"2496 33\n",
"2497 38\n",
"2498 48\n",
"2499 35\n",
"Name: Age, Length: 2500, dtype: int64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_netflix['Age']"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 2500 entries, 0 to 2499\n",
"Data columns (total 10 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 User ID 2500 non-null int64 \n",
" 1 Subscription Type 2500 non-null object\n",
" 2 Monthly Revenue 2500 non-null int64 \n",
" 3 Join Date 2500 non-null object\n",
" 4 Last Payment Date 2500 non-null object\n",
" 5 Country 2500 non-null object\n",
" 6 Age 2500 non-null int64 \n",
" 7 Gender 2500 non-null object\n",
" 8 Device 2500 non-null object\n",
" 9 Plan Duration 2500 non-null object\n",
"dtypes: int64(3), object(7)\n",
"memory usage: 195.4+ KB\n"
]
}
],
"source": [
"df_netflix.info()"
]
}
],
"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": "3.12.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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