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Excercise - 3
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‎Chapter 1/Excercises/Excercise_3_Impute_missing_data.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 1.Import the pandas library and load the dataset into the pandas data frame:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"\n",
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"#reading the data into the dataframe into the object data\n",
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"df = pd.read_csv('../Data/Banking_Marketing.csv', header=0)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 2.Print how many missing values on each column. To do so, use isna() function from pandas dataframe"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"age 2\n",
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"job 0\n",
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"marital 0\n",
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"education 0\n",
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"default 0\n",
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"housing 0\n",
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"loan 0\n",
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"contact 6\n",
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"month 0\n",
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"day_of_week 0\n",
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"duration 7\n",
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"campaign 0\n",
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"pdays 0\n",
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"previous 0\n",
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"poutcome 0\n",
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"emp_var_rate 0\n",
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"cons_price_idx 0\n",
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"cons_conf_idx 0\n",
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"euribor3m 0\n",
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"nr_employed 0\n",
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"y 0\n",
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"dtype: int64"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df.isna().sum()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 3.Impute the numerical data of the age column with its mean. To do so, first, find the mean of age column using the mean() function of pandas data frame and impute the missing data with its mean using fillna() function"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"40.023812413525256"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"mean_age = df.age.mean()\n",
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"mean_age"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"df.age.fillna(mean_age,inplace=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 4.Impute the numerical data of duration column with its median. To do so, first, find the median of duration column using the median() function of the pandas data frame and impute the missing data with its mean using fillna() function"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"180.0"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"median_duration = df.duration.median()\n",
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"median_duration"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"df. duration.fillna(median_duration,inplace=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 5.Impute the categorical data of the contact column with its mode. To do so, first, find the mode of contact column using mode() function of pandas data frame and impute the missing data with its mode using fillna() function"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'cellular'"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"mode_contact = df.contact.mode()[0]\n",
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"mode_contact"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"df.contact.fillna(mode_contact,inplace=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 6.Print how many missing values on each column. To do so, use isna() function from pandas dataframe"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"age 0\n",
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"job 0\n",
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"marital 0\n",
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"education 0\n",
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"default 0\n",
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"housing 0\n",
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"loan 0\n",
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"contact 0\n",
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"month 0\n",
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"day_of_week 0\n",
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"duration 0\n",
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"campaign 0\n",
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"pdays 0\n",
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"previous 0\n",
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"poutcome 0\n",
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"emp_var_rate 0\n",
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"cons_price_idx 0\n",
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"cons_conf_idx 0\n",
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"euribor3m 0\n",
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"nr_employed 0\n",
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"y 0\n",
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"dtype: int64"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df.isna().sum()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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