Анализ датасета с помощью Matplotlib.ipynb

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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![logo.png](attachment:logo.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PoZUv73C-M0K"
   },
   "source": [
    "## Анализ датасета Churn modeling\n",
    "\n",
    "https://www.kaggle.com/shubh0799/churn-modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
       "0          619    France  Female   42       2       0.00              1   \n",
       "1          608     Spain  Female   41       1   83807.86              1   \n",
       "2          502    France  Female   42       8  159660.80              3   \n",
       "3          699    France  Female   39       1       0.00              2   \n",
       "4          850     Spain  Female   43       2  125510.82              1   \n",
       "\n",
       "   IsActiveMember  EstimatedSalary  Exited  \n",
       "0               1        101348.88       1  \n",
       "1               1        112542.58       0  \n",
       "2               0        113931.57       1  \n",
       "3               0         93826.63       0  \n",
       "4               1         79084.10       0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Загружаем данные с помощью pandas\n",
    "cols = ['CreditScore', 'Geography', 'Gender', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'IsActiveMember', 'EstimatedSalary',\n",
    "'Exited']\n",
    "df_churn = pd.read_csv(\"data/Churn_Modelling.csv\", usecols=cols)\n",
    "df_churn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['France', 'Germany', 'Spain'], dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_churn['Geography'].value_counts().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5014, 2509, 2477])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_churn['Geography'].value_counts().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 3 artists>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Количество клиентов в каждой стране\n",
    "plt.figure(figsize=(8,5))\n",
    "plt.title(\"Number of Customers\", fontsize=14)\n",
    "plt.bar(x= df_churn['Geography'].value_counts().index,\n",
    "        height=df_churn.Geography.value_counts().values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x7fac6f0cf5e0>,\n",
       "  <matplotlib.axis.YTick at 0x7fac6f0c7e20>,\n",
       "  <matplotlib.axis.YTick at 0x7fac6f0c7460>,\n",
       "  <matplotlib.axis.YTick at 0x7fac6f2429d0>,\n",
       "  <matplotlib.axis.YTick at 0x7fac6f24e160>,\n",
       "  <matplotlib.axis.YTick at 0x7fac6f24e8b0>,\n",
       "  <matplotlib.axis.YTick at 0x7fac6f253100>],\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Редактируем ОСИ\n",
    "plt.figure(figsize=(8,5))\n",
    "plt.xticks(fontsize=12, rotation=45)\n",
    "plt.title(\"Number of Customers\", fontsize=14)\n",
    "plt.bar(x= df_churn['Geography'].value_counts().index,\n",
    "        height=df_churn.Geography.value_counts().values)\n",
    "plt.yticks(ticks=np.arange(0, 7000, 1000), fontsize=12)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3.623e+03, 6.900e+01, 3.600e+02, 1.173e+03, 2.081e+03, 1.747e+03,\n",
       "        7.290e+02, 1.860e+02, 3.000e+01, 2.000e+00]),\n",
       " array([     0.   ,  25089.809,  50179.618,  75269.427, 100359.236,\n",
       "        125449.045, 150538.854, 175628.663, 200718.472, 225808.281,\n",
       "        250898.09 ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Гистограмма балансов счетов\n",
    "plt.hist(x=df_churn['Balance'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution on Balance (25000 - 225000)')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Гистограмма балансов счетов c кастомизацией\n",
    "\n",
    "plt.hist(x=df_churn['Balance'], bins=12, color='darkgrey',\n",
    "         range=(25000, 225000))\n",
    "plt.title(\"Distribution on Balance (25000 - 225000)\", fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fac6f426190>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Точечные графики (scatter plot)\n",
    "sample = df_churn.sample(n=200, random_state=42) # подмножество исходного датасета\n",
    "plt.scatter(x=sample['CreditScore'], y=sample['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 611., 2179., 3629., 1871.,  828.,  523.,  208.,  127.,   20.,\n",
       "           4.]),\n",
       " array([18. , 25.4, 32.8, 40.2, 47.6, 55. , 62.4, 69.8, 77.2, 84.6, 92. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Гистограмма балансов счетов\n",
    "plt.hist(x=df_churn['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fac6f4615b0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Несколько графиков на одном рисунке\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "plt.title(\"France vs Germany\", fontsize=14)\n",
    "ax.scatter(x=sample[sample.Geography == 'France']['CreditScore'], y=sample[sample.Geography == 'France']['Age'])\n",
    "ax.scatter(x=sample[sample.Geography == 'Germany']['CreditScore'], y=sample[sample.Geography == 'Germany']['Age'])\n",
    "ax.legend(labels=['France','Germany'], loc='lower left', fontsize=12)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of Products')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Несколько графиков на одном рисунке\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, sharey=True,\n",
    "figsize=(8,5))\n",
    "countries = df_churn.Geography.value_counts()\n",
    "products = df_churn.NumOfProducts.value_counts()\n",
    "ax1.bar(x=countries.index, height=countries.values)\n",
    "ax1.set_title(\"Countries\", fontsize=12)\n",
    "ax2.bar(x=products.index, height=products.values)\n",
    "ax2.set_title(\"Number of Products\", fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "4.1.2. Примеры использования библиотеки Matplotlib.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.6"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}