Python Interview Prep: Questionnaire Guide Part 3

Published in

Women in Technology

4 min readJun 25, 2024

Welcome to the interview preparation guide series part 3. I have covered some of the Python coding FAQ in part 1 and part 2, here is the collection of dataframe related frequently asked Python coding interview questions. These questions will not just ace your interview but come in handy once you start working on real-time data sets.

Please solve these questionnaires independently and then compare the logic mentioned below. Please share any other solutions to these questions in the comment section.

Q1. Transform the categorical value true/false to 1/0 in a data frame without using one-hot encoding or get_dummies.

df = pd.DataFrame({'A': [True, False, True, False]})
df

Solution1:

df['A'] = df['A'].astype(int)
df

Solution 2:

df = pd.DataFrame({'A': [True, False, True, False]})
df['A'] = df['A'].map({True: 1, False: 0})

Solution 3:

df = pd.DataFrame({'A': [True, False, True, False]})
df['A'].replace([True, False],[1, 0], inplace=True)
df

Q2. Write a code to Sort a dataframe by two columns.

import pandas as pd
data = [['Tom', 10, 'UK'], ['Nick', 15, 'USA'], ['Juli', 14, 'Aus'], ['Atul', 10, 'IND'],]
df = pd.DataFrame(data, columns = ['Name', 'Age', 'Citizen'])
df.head()

df.sort_values(by = ['Name', 'Age'], inplace = True)
df

Q3. Find the row which has the maximum value of a given column.

df['Age'].idxmax()

output : 1

df.iloc[df['Age'].idxmax()]

Q4. How can you split a column that contains strings into multiple columns?

data = [['Tom class2', 10, 'UK'], ['Nick class5', 15, 'USA'], ['Juli class4', 14, 'Aus'], ['Atul class2', 10, 'IND'],]
df = pd.DataFrame(data, columns = ['Name', 'Age','Citizen'])
df_col = df['Name'].str.split(pat=" ", expand=True)
df[['Name', 'Class'] ]= df_col
df

Q5. Write a solution to report all the duplicate emails in the data frame column.

data = [['abc@gmail.com', 'abc'], ['abc1@gmail.com', 'abc'], ['abc@gmail.com', 'abc'], ['xyz@gmail.com', 'xyz'],['xyz@gmail.com', 'xyz']]
df = pd.DataFrame(data, columns = ['email id', 'name'])

def redundant_emailid(df, email_column):
    redt = df[email_column].value_counts()
    redt = redt[redt>1]
    return redt
    
redundant_emailid(df, 'email id')

Q6. How to check whether a dataframe is empty or not?

if df.empty:
    print("DF is empty")

Q7. Write a code to convert the date in the string format to date format.

from datetime import datetime    
    
# Define dates as the strings       
Day1 = 'Wednesday, July 14, 2018'    
Day2 = '15/7/17'    
Day3 = '16-07-2017'    
print("Type of Day1 is :" + str(type(Day1)))  

# Define dates as the datetime objects    
dmy_dt1 = datetime.strptime(Day1, '%A, %B %d, %Y')    
dmy_dt2 = datetime.strptime(Day2, '%d/%m/%y')    
dmy_dt3 = datetime.strptime(Day3, '%d-%m-%Y')    
    
#Print the converted dates    
print(dmy_dt1, type(dmy_dt1))    
print(dmy_dt2, type(dmy_dt2))    
print(dmy_dt3, type(dmy_dt2))

Output:

Type of Day1 is :<class ‘str’>
2018–07–14 00:00:00 <class ‘datetime.datetime’>
2017–07–15 00:00:00 <class ‘datetime.datetime’>
2017–07–16 00:00:00 <class ‘datetime.datetime’>

Q8. Write a program to implement different types of merge on two DataFrames.

merge() provides flexibility in merging dataframe and is best suited for complex merging conditions.

Syntax of merge:

https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html

Different types of merge:

data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana'], 
    'Age': [25, 30, 35, 40],
    'Department': ['HR', 'Finance', 'IT', 'Marketing']
}
df1 = pd.DataFrame(data)

data2 = {
    'Name': ['Alex', 'Bharat', 'Chaitra', 'Disha'], 
    'Age': [45, 32, 36, 47],
    'Department': ['RnD', 'Finance', 'Tech', 'Marketing']
}
df2 = pd.DataFrame(data2)

df_inner = pd.merge(df1, df2, on='Department', how='inner')
df_inner

df_outer = pd.merge(df1, df2, on='Department', how='outer')
df_outer

df_left = pd.merge(df1, df2, on='Department', how='left')
df_left

df_right = pd.merge(df1, df2, on='Department', how='right')
df_right

Python Interview Prep: Questionnaire Guide Part 1

Python Interview Prep: Questionnaire Guide Part 2

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