The Top 17 Data Frame Interview Questions You Need to Know in 2023

Data frames are one of the most important data structures in Python for data analysis, The pandas library provides a powerful and flexible DataFrame object that makes working with tabular data incredibly easy

As data frames are so critical for data science and analytics, you can expect to get questions about them during Python and data science interviews. In this post, I’ll walk through some of the most common data frame interview questions and provide sample answers to help you ace your next coding interview.

1. What is a data frame in Python?

A data frame is a two-dimensional, tabular data structure with labeled columns and rows. It is basically a table with columns of different data types like strings, integers, booleans, etc The pandas DataFrame object represents a data frame in Python.

Some key properties of a pandas DataFrame are:

• It can have columns with different data types (heterogenous data).
• Size is mutable – can add/delete rows and columns.
• Values are mutable – can modify data cells.
• Labeled axes – rows and columns have labels.
• Powerful indexing and slicing capabilities.

Data frames are a critical Python tool for data manipulation and analysis. They provide an intuitive way to organize, access and transform data.

2. How do you create a DataFrame in pandas?

There are many different ways to construct a DataFrame in pandas:

• From a single Series object.
• From a list of dicts with same keys.
• From a dict of Series objects.
• From a 2D NumPy array.
• From a NumPy structured array.
• From a DataFrame itself for duplication.
• From a list of tuples with column names provided.
• From a dict of tuples.
• By reading data from a file like CSV or Excel.

For example:

import pandas as pd# From list of dictsdata = [{'a': 1, 'b': 2}, {'a': 5, 'b': 10, 'c': 20}]df = pd.DataFrame(data) # From dict of Series d = {'one': pd.Series([1, 2, 3], index=['a', 'b', 'c']),     'two': pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])}df = pd.DataFrame(d)

The most flexible option is to pass a dict of {column_name: column_data}.

3. How do you select a column from a DataFrame?

We can select a column in pandas DataFrame in two ways:

1. Via column name: df['column_name']

2. Via column index: df.iloc[:,0]

For example:

import pandas as pddf = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}) # Select column via namedf['A'] # Select column via indexdf.iloc[:,0]

Selecting columns returns a Series object. We can also select multiple columns similarly by passing a list of column names or indices.

4. How do you select a row from a DataFrame?

To select a particular row from a DataFrame, we can use:

1. Via row index: df.iloc[0]

2. Via boolean indexing: df[df['column_name'] > 2]

For example:

# Select via index df.iloc[2]# Boolean indexing on 'A' column df[df['A'] > 2]

Row selection returns a Series object for the selected row. For boolean indexing, we pass a condition to filter rows.

5. How do you transform data in pandas DataFrame?

Pandas provides many flexible methods to transform data in DataFrames. Some useful ones are:

• map() – Map values according to input function.
• apply() – Apply function row-wise or column-wise.
• applymap() – Apply element-wise function.
• pipe() – Apply function to DataFrame via chained operations.
• agg() – Aggregate DataFrame by applying different functions to different columns.
• transform() – Transform DataFrame by applying function to each group.

For example:

df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})# Apply lambda function to square all values df.applymap(lambda x: x**2)# Apply sum function to 'B' columndf.agg({'B': 'sum'})

6. How do you handle missing data in pandas DataFrame?

There are several ways to deal with missing data (represented as NaN or None) in pandas:

• dropna() – Drops missing values rows/columns.
• fillna() – Fills missing values with replacements like mean, median etc.
• isnull() and notnull() – Checks for null values.
• interpolate() – Interpolates missing values by replacing NaN with linear interpolation.

For example:

df = pd.DataFrame({'A': [1, 2, None], 'B': [None, 5, 6]})# Fill NaN with mean of column df['A'].fillna(df['A'].mean())# Drop rows with any NaN valuesdf.dropna() # Forward fill NaN valuesdf.fillna(method='ffill')

Handling missing data avoids unwanted problems during analysis and modeling tasks.

7. How do you merge pandas DataFrames?

We can merge two DataFrames in pandas with merge() or join() methods.

Some examples:

df1 = pd.DataFrame({'A': ['A0', 'A1'], 'B': ['B0', 'B1']})df2 = pd.DataFrame({'A': ['A0', 'A2'], 'C': ['C0', 'C2']})# Merge df1 and df2 on 'A' column  df1.merge(df2, on='A')# Left join df1.merge(df2, how='left', on='A') # Join with 2 common columnsdf3.join(df4, on=['key1','key2']) 

The main parameters for merging are:

• on – Column to merge on.
• how – Type of join like inner, left, outer etc.
• suffixes – Suffixes to add to avoid duplicate column names.

8. How do you concatenate pandas DataFrames?

The concat() function allows us to concatenate or stack together DataFrames.

For example:

df1 = pd.DataFrame({'A': ['A0', 'A1'], 'B': ['B0', 'B1']})df2 = pd.DataFrame({'A': ['A2', 'A3'], 'B': ['B2', 'B3']})# Simply stack df1 on top of df2pd.concat([df1, df2])  # Stack with keyspd.concat([df1, df2], keys=['x', 'y']) # Stack side-by-sidepd.concat([df1, df2], axis=1)

Key differences between concat and merge:

• concat() stacks DataFrames row-wise or column-wise.
• merge() joins DataFrames based on logical conditions on columns.

9. How do you handle duplicate data in pandas DataFrame?

To deal with duplicate data in pandas DataFrame, we can use the following approaches:

• drop_duplicates() – Drops duplicate rows. Can pass columns to detect duplicates on.
• groupby() – Group by columns and apply aggregates on each group.
• count() – Count occurrences of each row.
• pivot_table() – Pivot, group and aggregate data to handle duplicates.
• unique() – Get unique values across requested axis.

For example:

df = pd.DataFrame({'A': ['x', 'y', 'x'], 'B': [1, 2, 3]})# Distinct rows onlydf.drop_duplicates()# Count duplicates  df.groupby('A').size()

Handling duplicates avoids statistical issues and gives unique or aggregated data.

10. How do you rename columns in pandas DataFrame?

There are a couple ways to rename DataFrame columns in pandas:

1. Use DataFrame rename() method:

df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})df.rename(columns={'A': 'a', 'B': 'b'})

1. Assign new names directly:

df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]}) df.