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Copyright by Pierian Data Inc. For more information, visit us at www.pieriandata.com

Seaborn Exercises

Imports

Run the cell below to import the libraries

In [1]:
import numpy as np
import pandas as pd
import seaborn as sns

import matplotlib.pyplot as plt

The Data

DATA SOURCE: https://www.kaggle.com/rikdifos/credit-card-approval-prediction

Data Information:

Credit score cards are a common risk control method in the financial industry. It uses personal information and data submitted by credit card applicants to predict the probability of future defaults and credit card borrowings. The bank is able to decide whether to issue a credit card to the applicant. Credit scores can objectively quantify the magnitude of risk.

Feature Information:

application_record.csv
Feature name Explanation Remarks
ID Client number
CODE_GENDER Gender
FLAG_OWN_CAR Is there a car
FLAG_OWN_REALTY Is there a property
CNT_CHILDREN Number of children
AMT_INCOME_TOTAL Annual income
NAME_INCOME_TYPE Income category
NAME_EDUCATION_TYPE Education level
NAME_FAMILY_STATUS Marital status
NAME_HOUSING_TYPE Way of living
DAYS_BIRTH Birthday Count backwards from current day (0), -1 means yesterday
DAYS_EMPLOYED Start date of employment Count backwards from current day(0). If positive, it means the person currently unemployed.
FLAG_MOBIL Is there a mobile phone
FLAG_WORK_PHONE Is there a work phone
FLAG_PHONE Is there a phone
FLAG_EMAIL Is there an email
OCCUPATION_TYPE Occupation
CNT_FAM_MEMBERS Family size
In [3]:
df = pd.read_csv('application_record.csv')
In [4]:
df.head()
Out[4]:
ID CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY CNT_CHILDREN AMT_INCOME_TOTAL NAME_INCOME_TYPE NAME_EDUCATION_TYPE NAME_FAMILY_STATUS NAME_HOUSING_TYPE DAYS_BIRTH DAYS_EMPLOYED FLAG_MOBIL FLAG_WORK_PHONE FLAG_PHONE FLAG_EMAIL OCCUPATION_TYPE CNT_FAM_MEMBERS
0 5008804 M Y Y 0 427500.0 Working Higher education Civil marriage Rented apartment -12005 -4542 1 1 0 0 NaN 2.0
1 5008805 M Y Y 0 427500.0 Working Higher education Civil marriage Rented apartment -12005 -4542 1 1 0 0 NaN 2.0
2 5008806 M Y Y 0 112500.0 Working Secondary / secondary special Married House / apartment -21474 -1134 1 0 0 0 Security staff 2.0
3 5008808 F N Y 0 270000.0 Commercial associate Secondary / secondary special Single / not married House / apartment -19110 -3051 1 0 1 1 Sales staff 1.0
4 5008809 F N Y 0 270000.0 Commercial associate Secondary / secondary special Single / not married House / apartment -19110 -3051 1 0 1 1 Sales staff 1.0
In [5]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 438557 entries, 0 to 438556
Data columns (total 18 columns):
 #   Column               Non-Null Count   Dtype  
---  ------               --------------   -----  
 0   ID                   438557 non-null  int64  
 1   CODE_GENDER          438557 non-null  object 
 2   FLAG_OWN_CAR         438557 non-null  object 
 3   FLAG_OWN_REALTY      438557 non-null  object 
 4   CNT_CHILDREN         438557 non-null  int64  
 5   AMT_INCOME_TOTAL     438557 non-null  float64
 6   NAME_INCOME_TYPE     438557 non-null  object 
 7   NAME_EDUCATION_TYPE  438557 non-null  object 
 8   NAME_FAMILY_STATUS   438557 non-null  object 
 9   NAME_HOUSING_TYPE    438557 non-null  object 
 10  DAYS_BIRTH           438557 non-null  int64  
 11  DAYS_EMPLOYED        438557 non-null  int64  
 12  FLAG_MOBIL           438557 non-null  int64  
 13  FLAG_WORK_PHONE      438557 non-null  int64  
 14  FLAG_PHONE           438557 non-null  int64  
 15  FLAG_EMAIL           438557 non-null  int64  
 16  OCCUPATION_TYPE      304354 non-null  object 
 17  CNT_FAM_MEMBERS      438557 non-null  float64
dtypes: float64(2), int64(8), object(8)
memory usage: 60.2+ MB

TASKS

Recreate the plots shown in the markdown image cells. Each plot also contains a brief description of what it is trying to convey. Note, these are meant to be quite challenging. Start by first replicating the most basic form of the plot, then attempt to adjust its styling and parameters to match the given image.

In general do not worry about coloring,styling, or sizing matching up exactly. Instead focus on the content of the plot itself. Our goal is not to test you on recognizing figsize=(10,8) , its to test your understanding of being able to see a requested plot, and reproducing it.

NOTE: You may need to perform extra calculations on the pandas dataframe before calling seaborn to create the plot.

TASK: Recreate the Scatter Plot shown below

The scatterplot attempts to show the relationship between the days employed versus the age of the person (DAYS_BIRTH) for people who were not unemployed. Note, to reproduce this chart you must remove unemployed people from the dataset first. Also note the sign of the axis, they are both transformed to be positive. Finally, feel free to adjust the alpha and linewidth parameters in the scatterplot since there are so many points stacked on top of each other.

In [3]:
# CODE HERE TO RECREATE THE PLOT SHOWN ABOVE
In [ ]:

TASK: Recreate the Distribution Plot shown below:

Note, you will need to figure out how to calculate "Age in Years" from one of the columns in the DF. Think carefully about this. Don't worry too much if you are unable to replicate the styling exactly.

In [1]:
# CODE HERE TO RECREATE THE PLOT SHOWN ABOVE
In [ ]:

TASK: Recreate the Categorical Plot shown below:

This plot shows information only for the bottom half of income earners in the data set. It shows the boxplots for each category of NAME_FAMILY_STATUS column for displaying their distribution of their total income. Note: You will need to adjust or only take part of the dataframe before recreating this plot. You may want to explore the order parameter to get the xticks in the exact order shown here

In [52]:
# CODE HERE

TASK: Recreate the Heat Map shown below:

This heatmap shows the correlation between the columns in the dataframe. You can get correlation with .corr() , also note that the FLAG_MOBIL column has NaN correlation with every other column, so you should drop it before calling .corr().

In [1]:
# CODE HERE
In [22]:
Out[22]:
<AxesSubplot:>
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