import pandas as pd #for data manipulations import numpy as np # for computations import seaborn as sns # for visualization import matplotlib.pyplot as plt # for visualizations import warnings warnings.filterwarnings('ignore') %matplotlib inline pd.set_option("display.max_colwidth", 50)

Loading data

data = pd.read_csv("data/df.csv")

# Let's preview the datasets data.head()

countryobject

yearint64

Kenya

2018

Kenya

2018

Kenya

2018

Kenya

2018

Kenya

2018

As we can observe that our data frame has proper standard names and we can understand what they do mean no need of renaming our columns

# let's see the shape of datasets data.shape

The dataset contain 23524 records and 13 columns

# To understand what each column , lets use variable definition file var_def = pd.read_csv("data/VariableDefinitions.csv") var_def.T

0object

1object

Variable Definitions

country

year

Unnamed: 1

Country interviewee is in.

Year survey was done in.

`var_def.T` gives records in a transpose manner, which is the best to see each record in small space rather than occupying large space

# let's see information of our dataset data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 23524 entries, 0 to 23523
Data columns (total 13 columns):
 #   Column                  Non-Null Count  Dtype 
---  ------                  --------------  ----- 
 0   country                 23524 non-null  object
 1   year                    23524 non-null  int64 
 2   uniqueid                23524 non-null  object
 3   bank_account            23524 non-null  object
 4   location_type           23524 non-null  object
 5   cellphone_access        23524 non-null  object
 6   household_size          23524 non-null  int64 
 7   age_of_respondent       23524 non-null  int64 
 8   gender_of_respondent    23524 non-null  object
 9   relationship_with_head  23524 non-null  object
 10  marital_status          23524 non-null  object
 11  education_level         23524 non-null  object
 12  job_type                23524 non-null  object
dtypes: int64(3), object(10)
memory usage: 2.3+ MB

# Let's check for missing data data.isnull().sum().any()

Univariate Analysis

# Let's start explore age distribution using histogram plt.figure(figsize=(14,7)) data.age_of_respondent.hist() plt.title("Age of Respondent Distribution") plt.xlabel('age_of_respondent') plt.show()

# Let's start explore household_size distribution using histogram plt.figure(figsize=(14,7)) data.household_size.hist() plt.title("Household Size Distribution") plt.xlabel('household_size') plt.show()

# Let's explore the distribution of our target column using count sns.catplot(x="bank_account", kind="count", data=data) plt.title("Banck Account Distribution")

# Let's explore the distribution of Gender of respondents using count sns.catplot(x="gender_of_respondent", kind="count", data=data) plt.title("Gender of Respondent Distribution")

# Let's explore the distribution of country of respondents using count sns.catplot(x="country", kind="count", data=data) plt.title("Country of Respondent Distribution")

# Let's explore the distribution of Year of respondents using count sns.catplot(x="year", kind="count", data=data) plt.title("Year Distribution")

# Let's explore the distribution of location_type of respondents using count sns.catplot(x="location_type", kind="count", data=data) plt.title("Location Distribution")

# Let's explore the distribution of cellphone_access using count sns.catplot(x="cellphone_access", kind="count", data=data) plt.title("Cellphone Acess Distribution")

# Let's explore the distribution of Education level using count sns.catplot(x="education_level", kind="count", data=data) plt.title("Education Level Distribution") plt.xticks( rotation=45, horizontalalignment='right', fontweight='light', fontsize='x-large' )

# Let's explore the distribution of Job Type using count sns.catplot(x="job_type", kind="count", data=data) plt.title("Job Type Distribution") plt.xticks( rotation=45, horizontalalignment='right', fontweight='light', fontsize='x-large' )

# Let's explore the distribution of marital_status using count sns.catplot(x="marital_status", kind="count", data=data) plt.title("Marital Status Distribution") plt.xticks( rotation=45, horizontalalignment='right', fontweight='light', fontsize='x-large' )