Data_Exploration_and_Cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('default_of_credit_card_clients.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(seed=24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_integers = np.random.randint(low=1,high=5,size=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "is_equal_to_3 = random_integers == 3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False, False, False, False])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_equal_to_3[:5]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(is_equal_to_3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_integers[is_equal_to_3]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8d1b6de3-9795    True\n",
       "7574bf9e-f3f9    True\n",
       "8f9549b7-d5a4    True\n",
       "62b1d551-2414    True\n",
       "ab09b4b4-8331    True\n",
       "Name: ID, dtype: bool"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id_counts = df['ID'].value_counts()\n",
    "dupe_mask = id_counts == 2\n",
    "dupe_mask[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "313"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dupe_ids = id_counts.index[dupe_mask]\n",
    "dupe_ids = list(dupe_ids)\n",
    "len(dupe_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
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       "      <td>214062</td>\n",
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       "      <td>16117</td>\n",
       "      <td>20516</td>\n",
       "      <td>7092</td>\n",
       "      <td>6019</td>\n",
       "      <td>4636</td>\n",
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       "<p>6 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE PAY_1  PAY_2  \\\n",
       "6757   7574bf9e-f3f9      30000    1          2         2   24     0      0   \n",
       "6857   7574bf9e-f3f9          0    0          0         0    0     0      0   \n",
       "23924  8d1b6de3-9795     420000    1          2         1   24     0      0   \n",
       "24024  8d1b6de3-9795          0    0          0         0    0     0      0   \n",
       "28834  8f9549b7-d5a4     220000    2          1         2   36     0      0   \n",
       "28934  8f9549b7-d5a4          0    0          0         0    0     0      0   \n",
       "\n",
       "       PAY_3  PAY_4  ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  \\\n",
       "6757       0     -1  ...        517      25420      24863      1545         0   \n",
       "6857       0      0  ...          0          0          0         0         0   \n",
       "23924      0      0  ...     214062     164003     162836     16117     20516   \n",
       "24024      0      0  ...          0          0          0         0         0   \n",
       "28834      0      0  ...     221193     181859     184605     10000      8018   \n",
       "28934      0      0  ...          0          0          0         0         0   \n",
       "\n",
       "       PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  default payment next month  \n",
       "6757       1517     25420         0       980                           0  \n",
       "6857          0         0         0         0                           0  \n",
       "23924      7092      6019      4636      5570                           0  \n",
       "24024         0         0         0         0                           0  \n",
       "28834     10121      6006     10987    143779                           1  \n",
       "28934         0         0         0         0                           0  \n",
       "\n",
       "[6 rows x 25 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['ID'].isin(dupe_ids[0:3]),:].head(10) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_zero_mask = df == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "315"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_zero_mask = df_zero_mask.iloc[:,1:].all(axis=1)\n",
    "sum(feature_zero_mask)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean_1 = df.loc[~feature_zero_mask,:].copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29685"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_1['ID'].nunique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    True\n",
       "1    True\n",
       "2    True\n",
       "3    True\n",
       "4    True\n",
       "Name: PAY_1, dtype: bool"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_pay_1_mask = df_clean_1['PAY_1'] != 'Not available'\n",
    "valid_pay_1_mask[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean_2 = df_clean_1.loc[valid_pay_1_mask,:].copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 26664 entries, 0 to 29999\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype\n",
      "---  ------  --------------  -----\n",
      " 0   PAY_1   26664 non-null  int64\n",
      " 1   PAY_2   26664 non-null  int64\n",
      "dtypes: int64(2)\n",
      "memory usage: 624.9 KB\n"
     ]
    }
   ],
   "source": [
    "df_clean_2['PAY_1'] = df_clean_2['PAY_1'].astype('int64')\n",
    "df_clean_2[['PAY_1', 'PAY_2']].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    12458\n",
       "1     9412\n",
       "3     4380\n",
       "4      414\n",
       "Name: EDUCATION, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_2['EDUCATION'].replace(to_replace=[0, 5, 6], value=4, inplace=True)\n",
    "df_clean_2['EDUCATION'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    14158\n",
       "1    12172\n",
       "3      334\n",
       "Name: MARRIAGE, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_2['MARRIAGE'].replace(to_replace=0, value=3, inplace=True)\n",
    "df_clean_2['MARRIAGE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean_2['EDUCATION_CAT'] = 'none'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>EDUCATION_CAT</th>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
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       "      <th>5</th>\n",
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       "      <th>6</th>\n",
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       "      <td>none</td>\n",
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       "      <th>7</th>\n",
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       "      <td>none</td>\n",
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       "      <th>8</th>\n",
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       "      <th>9</th>\n",
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      ],
      "text/plain": [
       "   EDUCATION EDUCATION_CAT\n",
       "0          2          none\n",
       "1          2          none\n",
       "2          2          none\n",
       "3          2          none\n",
       "4          2          none\n",
       "5          1          none\n",
       "6          1          none\n",
       "7          2          none\n",
       "8          3          none\n",
       "9          3          none"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_2[['EDUCATION', 'EDUCATION_CAT']].head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_mapping = {\n",
    "    1: \"graduate school\",\n",
    "    2: \"university\",\n",
    "    3: \"high school\",\n",
    "    4: \"others\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>EDUCATION_CAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>university</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>university</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>university</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>university</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>university</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>graduate school</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>graduate school</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>university</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>high school</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>high school</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   EDUCATION    EDUCATION_CAT\n",
       "0          2       university\n",
       "1          2       university\n",
       "2          2       university\n",
       "3          2       university\n",
       "4          2       university\n",
       "5          1  graduate school\n",
       "6          1  graduate school\n",
       "7          2       university\n",
       "8          3      high school\n",
       "9          3      high school"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_2['EDUCATION_CAT'] = df_clean_2['EDUCATION'].map(cat_mapping)\n",
    "df_clean_2[['EDUCATION', 'EDUCATION_CAT']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "edu_ohe = pd.get_dummies(df_clean_2['EDUCATION_CAT'])\n",
    "edu_ohe.head(10)\n",
    "df_with_ohe = pd.concat([df_clean_2, edu_ohe], axis=1)\n",
    "df_with_ohe[['EDUCATION_CAT', 'graduate school',\n",
    "             'high school', 'university', 'others']].head(10)\n",
    "df_with_ohe.to_csv('cleaned_data.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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