Python Pandas Interview Questions and Answers
Intermediate / 1 to 5 years experienced level questions & answers
Ques 1. Explain the DataFrame in Pandas.
A DataFrame is a 2-dimensional labeled data structure with columns that can be of different types. It is similar to a spreadsheet or SQL table.
Ques 2. What is the difference between loc and iloc in Pandas?
loc is label-based indexing, and iloc is integer-based indexing.
Ques 3. Explain the use of the groupby function in Pandas.
groupby is used to split the data into groups based on some criteria and then apply a function to each group independently.
Example:
df.groupby('column1').mean()
Ques 4. How do you handle missing data in a DataFrame?
df.dropna() or df.fillna(value)
Ques 5. What is the purpose of the merge function in Pandas?
merge is used to combine two DataFrames based on a common column or index.
Example:
pd.merge(df1, df2, on='common_column')
Ques 6. What is the purpose of the melt function in Pandas?
melt is used to transform wide-format data to long-format data.
Example:
pd.melt(df, id_vars=['id_column'], value_vars=['value_column'])
Ques 7. Explain the concept of broadcasting in Pandas.
Broadcasting is the ability of NumPy and Pandas to perform operations on arrays or DataFrames of different shapes.
Ques 8. What is the purpose of the concat function in Pandas?
concat is used to concatenate DataFrames along a particular axis.
Example:
pd.concat([df1, df2], axis=1)
Ques 9. What is the purpose of the nunique function in Pandas?
nunique returns the number of unique elements in a Series or DataFrame.
Example:
df['column'].nunique()
Ques 10. Explain the use of the cut function in Pandas.
cut is used to segment and sort data values into bins.
Example:
pd.cut(df['column'], bins=[0, 25, 50, 75, 100])
Ques 11. Explain the concept of method chaining in Pandas.
Method chaining is a way of applying multiple operations on a DataFrame in a single line of code.
Example:
df.dropna().mean()
Ques 12. What is the purpose of the iterrows() function in Pandas?
iterrows() is used to iterate over DataFrame rows as (index, Series) pairs.
Example:
for index, row in df.iterrows():
print(index, row['column'])
Ques 13. Explain the use of the get_dummies() function in Pandas.
get_dummies() is used to convert categorical variable(s) into dummy/indicator variables.
Example:
pd.get_dummies(df['column'])
Ques 14. What is the difference between Series.value_counts() and DataFrame['column'].value_counts()?
Series.value_counts() returns the counts of unique values in a Series, while DataFrame['column'].value_counts() returns counts for a specific column.
Ques 15. What is the purpose of the pd.to_numeric() function?
pd.to_numeric() is used to convert argument to a numeric type.
Example:
df['column'] = pd.to_numeric(df['column'], errors='coerce')
Ques 16. Explain the use of the pd.cut() function with the `bins` parameter.
pd.cut() is used to segment and sort data values into bins. The `bins` parameter defines the bin edges.
Example:
pd.cut(df['column'], bins=[0, 25, 50, 75, 100])
Ques 17. How can you merge two DataFrames based on multiple columns?
Use the on parameter with a list of column names. pd.merge(df1, df2, on=['column1', 'column2'])
Ques 18. How do you pivot a Pandas DataFrame using the pivot() function?
Use the pivot() function to reshape the DataFrame based on column values.
Example:
df.pivot(index='index_column', columns='column_to_pivot', values='value_column')
Ques 19. What is the purpose of the crosstab() function in Pandas?
crosstab() computes a simple cross-tabulation of two (or more) factors.
Example:
pd.crosstab(df['factor1'], df['factor2'])
Ques 20. How do you apply a custom function to each element in a Pandas DataFrame?
Use the applymap() function. df.applymap(my_function)
Ques 21. Explain the concept of method chaining in Pandas.
Method chaining is a way of applying multiple operations on a DataFrame in a single line of code.
Example:
df.dropna().mean()
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