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Pandas Interview Questions
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Pandas Basic Interview Questions
1. What is Pandas in Python?
2. Mention the different types of Data Structures in Pandas?
3. What are the significant features of the pandas Library?
4. Define Series in Pandas?
5. Define DataFrame in Pandas?
6. What are the different ways in which a series can be created?
7. What are the different ways in which a dataframe can be created?
8. How can we create a copy of the series in Pandas?
9. Explain Categorical data in Pandas?
10. Explain Reindexing in pandas along with its parameters?
11. What is NumPy?
12. Give a brief description of time series in Panda?
13. Explain MultiIndexing in Pandas.
14. How can we convert Series to DataFrame?
15. How can we convert DataFrame to Numpy Array?
16. How can we convert DataFrame to an excel file?
17. What is TimeDelta?
18. Explain Pandas Timedelta.seconds Property
Pandas Interview Questions for Experienced
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Contents

Pandas Interview Questions for
Experienced (.....Continued)
19. Is iterating over a Pandas Dataframe a good practice? If not what are the
important conditions to keep in mind before iterating?
20. How would you iterate over rows in a DataFrame in Pandas?
21. List some statistical functions in Python Pandas?
22. How to Read Text Files with Pandas?
23. How are iloc() and loc() different?
24. How will you sort a DataFrame?
25. How would you convert continuous values into discrete values in Pandas?
26. What is the difference between join() and merge() in Pandas?
27. What is the difference(s) between merge() and concat() in Pandas?
28. What’s the difference between interpolate() and fillna() in Pandas?
Pandas Coding Interview Questions
29. How to set Index to a Pandas DataFrame?
30. How to add a row to a Pandas DataFrame?
31. How to add a column to a Pandas DataFrame?
32. How can we convert DataFrame into a NumPy array?
33. How will you compute the percentile of a numerical series in Pandas?
34. How to create Timedelta objects in Pandas?
35. How do you split a DataFrame according to a boolean criterion?
36. How can we convert NumPy array into a DataFrame?

Pandas Coding Interview Questions (.....Continued)
37. How to delete a row in Pandas DataFrame?
38. How to delete a column in Pandas DataFrame?
39. How to get frequency count of unique items in a Pandas DataFrame?
40. How to rename the index in a Pandas DataFrame?
41. How to reset the index in a Python Pandas DataFrame?
Pandas Interview Questions for Data Scientists
42. How can you find the row for which the value of a specific column is max or min?
43. Explain the GroupBy function in Pandas
44. How to format data in your Pandas DataFrame?
45. What is the use of pandas.Dataframe.aggregate() function? Explain its syntax
and parameters.
46. How to get items of series A not present in series B?
47. Describe a few data operations in Pandas.
48. Compare the Pandas methods: map(), applymap(), apply()
49. What’s the diﬀerence between pivot_table() and groupby()?
50. When to use merge() over concat() and vice-versa in Pandas?

Introduction
The Pandas Interview Questions and Answers revolve around the tool’s features, data
structures, and functions in Python interviews. It is widely used in data science and
machine learning projects, as well as in industries such as finance, healthcare, and
marketing. Pandas provides a wide range of functionalities, including data loading,
cleaning, filtering, transforming, merging, grouping, and aggregating.
For those seeking a career in data science or related fields, it’s important to have a
good understanding of Pandas and their applications. Therefore, it’s common for job
interviews in these fields to include questions about Pandas. These questions can
range from basic to advanced and cover various topics, such as data structures,
indexing, merging and joining, groupby operations, and time series analysis.
Whether you are a beginner or an experienced Python programmer, this article will
help you prepare for your next Pandas-related job interview. In this article, we will
explore some commonly asked Pandas Interview Questions and Answers which are
divided into the following sections:
Pandas MCQ Questions
1. What is Pandas in Python?
Let's get Started

Pandas is an open-source Python package that is most commonly used for data
science, data analysis, and machine learning tasks. It is built on top of another library
named Numpy. It provides various data structures and operations for manipulating
numerical data and time series and is very efficient in performing various functions
like data visualization, data manipulation, data analysis, etc.
2. Mention the different types of Data Structures in Pandas?
Pandas have three different types of data structures. It is due to these simple and
flexible data structures that it is fast and efficient.
Series - It is a one-dimensional array-like structure with homogeneous data
which means data of different data types cannot be a part of the same series. It
can hold any data type such as integers, floats, and strings and its values are
mutable i.e. it can be changed but the size of the series is immutable i.e. it
cannot be changed.
DataFrame - It is a two-dimensional array-like structure with heterogeneous
data. It can contain data of different data types and the data is aligned in a
tabular manner. Both size and values of DataFrame are mutable.
Panel - The Pandas have a third type of data structure known as Panel, which is a
3D data structure capable of storing heterogeneous data but it isn’t that
widely used.
3. What are the significant features of the pandas Library?
Pandas library is known for its efficient data analysis and state-of-the-art data
visualization.
The key features of the panda’s library are as follows:

Fast and efficient DataFrame object with default and customized indexing.
High-performance merging and joining of data.
Data alignment and integrated handling of missing data.
Label-based slicing, indexing, and subsetting of large data sets.
Reshaping and pivoting of data sets.
Tools for loading data into in-memory data objects from different file formats.
Columns from a data structure can be deleted or inserted.
Group by data for aggregation and transformations.
Time Series functionality.
4. Define Series in Pandas?
It is a one-dimensional array-like structure with homogeneous data which means
data of different data types cannot be a part of the same series. It can hold any data
type such as integers, floats, and strings and its values are mutable i.e. it can be
changed but the size of the series is immutable i.e. it cannot be changed. By using a
‘series’ method, we can easily convert the list, tuple, and dictionary into a series. A
Series cannot contain multiple columns.
5. Define DataFrame in Pandas?
It is a two-dimensional array-like structure with heterogeneous data. It can contain
data of different data types and the data is aligned in a tabular manner i.e. in rows
and columns and the indexes with respect to these are called row index and column
index respectively. Both size and values of DataFrame are mutable. The columns can
be heterogeneous types like int and bool. It can also be defined as a dictionary of
Series.
The syntax for creating a dataframe:
import pandas as pd
dataframe = pd.DataFrame( data, index, columns, dtype)
Here:

data - It represents various forms like series, map, ndarray, lists, dict, etc.
index - It is an optional argument that represents an index to row labels.
columns - Optional argument for column labels.
Dtype - It represents the data type of each column. It is an optional parameter.
6. What are the diﬀerent ways in which a series can be created?
There are diﬀerent ways of creating a series in Pandas.
Creating an empty Series: The simplest series that can be created is an empty
series. The Series() function of Pandas is used to create a series of any kind.
Code Example 1:
# import pandas as pd
import pandas as pd
# Creating empty Series
ser = pd.Series()
print(ser)
Output:
Series([], dtype: float64)
Creating a series from an array: Pandas is built on top of the Numpy library. In
order to create a series from the NumPy array, we have to import the NumPy
module and have to use numpy.array() the function.
Code Example 2:

import pandas as pd
# import numpy as np
import numpy as np
# simple array
data = np.array(['s', 'c', 'a', 'l', 'a','r'])
ser = pd.Series(data)
print(ser)
Output:
0 s
1 c
2 a
3 l
4 a
5 r
dtype: object
Creating a series from the array with an index: In order to create a series by
exclusively providing an index instead of the default value we need to provide a
list of elements to the index parameter with the same number of elements as
given in the array.
Code Example 3:
import pandas as pd
# import numpy as np
import numpy as np
# simple array
data = np.array(['s', 'c', 'a', 'l', 'a','r'])
# providing an index
ser = pd.Series(data, index=[10, 11, 12, 13, 14,15])
print(ser)

Output:
10 s
11 c
12 a
13 l
14 a
15 r
dtype: object
Creating a series from Lists: In order to create a series from a list, the first step
is to create a list, and then we need to create a series from the given list.
Code Example 4:
import pandas as pd
# a simple list
list = ['s', 'c', 'a', 'l', 'a','r']
# create series form a list
ser = pd.Series(list)
print(ser)
Output:
0 s
1 c
2 a
3 l
4 a
5 r
dtype: object
Creating a series from Dictionary: In order to create a series from the
dictionary, the first step is to create a dictionary, and only then we can create a
series using. The dictionary keys serve as indexes for the Series.
Code Example 5:

import pandas as pd
# a simple dictionary
dict = {'A': 101,
'B': 202,
'C': 303}
# create series from dictionary
ser = pd.Series(dict)
print(ser)
Output:
A 101
B 202
C 303
dtype: int64
Creating a series from Scalar value: In order to create a series from scalar
value, an index must be provided. The value repeats itself to fit the length of the
series or index given in general.
Code Example 6:
import pandas as pd
import numpy as np
# giving a scalar value with index
ser = pd.Series(10, index=[0, 1, 2, 3, 4, 5])
print(ser)
Output:

0 10
1 10
2 10
3 10
4 10
5 10
dtype: int64
7. What are the diﬀerent ways in which a dataframe can be
created?
Creating an empty dataframe: A basic DataFrame, which can be created is an
Empty Dataframe. An Empty Dataframe is created just by calling a
pandas.DataFrame() constructor.
Code Example :
# Importing Pandas to create DataFrame
import pandas as pd
# Creating Empty DataFrame and Storing it in variable df
df = pd.DataFrame()
# Printing Empty DataFrame
print(df)
Output:
Empty DataFrame
Columns: []
Index: []
Creating a dataframe using List: DataFrame can be created using a single list
or by using a list of lists.
Code Example :

# Import pandas library
import pandas as pd
# initialize list elements
data = [110,202,303,404,550,650]
# Create the pandas DataFrame with the column name provided explicitly
df = pd.DataFrame(data, columns=['Amounts'])
# print dataframe.
print(df)
Output:
Amounts
0 110
1 202
2 303
3 404
4 550
5 650
Code Example :
# Import pandas library
import pandas as pd
# initialize list of lists
data = [['mark', 20], ['zack', 16], ['ron', 24]]
# Create the pandas DataFrame
df = pd.DataFrame(data, columns=['Name', 'Age'])
# print dataframe.
print(df)
Output:
Name Age
0 mark 20
1 zack 16
2 ron 24

Creating DataFrame from dict of ndarray/lists: To create a DataFrame from
dict of narray/list there are a few conditions to be met.
First, all the arrays must be of the same length.
Second, if the index is passed then the length index should be equal to the
length of arrays.
Third, if no index is passed, then by default, the index will be in the range( n )
where n is the length of the array.
Code Example :
# Python code demonstrates creating
# DataFrame from dict narray / lists
# By default addresses.
import pandas as pd
# initialize data of lists.
data = {'Name': ['Max', 'Lara', 'Koke', 'muller'],
'Age': [10, 31, 91, 48]}
# Create DataFrame
df = pd.DataFrame(data)
# Print the output.
print(df)
Output:
Name Age
0 Max 10
1 Lara 31
2 Koke 91
3 muller 48
Create pandas dataframe from lists using a dictionary: Creating pandas
DataFrame from lists using a dictionary can be achieved in multiple ways. We
can create pandas DataFrame from lists using a dictionary by using
pandas.DataFrame() .
Code Example :

# Python code demonstrates how to create
# Pandas DataFrame by lists of dicts.
import pandas as pd
# Initialize data to lists.
data = [{'aa': 1, 'bs': 2, 'cd': 3},
{'aa': 10, 'bs': 20, 'cd': 30}]
# Creates DataFrame.
# Print the data
print(df)
Output:
aa bs cd
0 1 2 3
1 10 20 30
Creating dataframe from series: In order to create a dataframe using series the
argument to be passed in a DataFrame() function has to be a Series.
Code Example:
# Python code demonstrates creating
# Pandas Dataframe from series.
import pandas as pd
# Initialize data to series.
d = pd.Series([10, 20, 30, 40])
# creates Dataframe.
df = pd.DataFrame(d)
# print the data.
print(df)
Output:

0
0 10
1 20
2 30
3 40
Creating DataFrame from Dictionary of series: To create a DataFrame from
Dict of series, a dictionary needs to be passed as an argument to form a
DataFrame. The resultant index is the union of all the series of passed indexed.
Code Example :
# Python code demonstrate creating
# Pandas Dataframe from Dicts of series.
import pandas as pd
# Initialize data to Dicts of series.
d = {'one': pd.Series([10, 20, 30, 40],
index=['a', 'b', 'c', 'd']),
'two': pd.Series([10, 20, 30, 40],
index=['a', 'b', 'c', 'd'])}
# creates Dataframe.
df = pd.DataFrame(d)
# print the data.
print(df)
Output:
one two
a 10 10
b 20 20
c 30 30
d 40 40
8. How can we create a copy of the series in Pandas?
We can create a copy of the series by using the following syntax:
Series.copy(deep=True)

The default value for the deep parameter is set to True.
When the value of deep=True , the creation of a new object with a copy of the calling
object’s data and indices takes place. Modifications to the data or indices of the copy
will not be reflected in the original object whereas when the value of deep=False ,
the creation of a new object will take place without copying the calling object’s data
or index i.e. only the references to the data and index will be copied. Any changes
made to the data of the original object will be reflected in the shallow copy and vice
versa.
9. Explain Categorical data in Pandas?
Categorical data is a discrete set of values for a particular outcome and has a fixed
range. Also, the data in the category need not be numerical, it can be textual in
nature. Examples are gender, social class, blood type, country aﬀiliation, observation
time, etc. There is no hard and fast rule for how many values a categorical value
should have. One should apply one’s domain knowledge to make that determination
on the data sets.
10. Explain Reindexing in pandas along with its parameters?
Reindexing as the name suggests is used to alter the rows and columns in a
DataFrame. It is also defined as the process of conforming a dataframe to a new
index with optional filling logic. For missing values in a dataframe, the reindex()
method assigns NA/NaN as the value. A new object is returned unless a new index is
produced that is equivalent to the current one. The copy value is set to False. This is
also used for changing the index of rows and columns in the dataframe.
11. What is NumPy?
NumPy is one of the most widely used, versatile, simple, open-source, python-based,
general-purpose packages that is used for processing arrays. NumPy is an
abbreviation for NUMerical PYthon. Due to its highly optimized tools, it provides
high-performance and powerful N -dimensional array processing capabilities that
are explicitly designed to handle complex arrays. It is most commonly used in
performing scientific computations and various broadcasting functions because of its
popularity, powerful performance, and flexibility to perform various operations.

12. Give a brief description of time series in Panda?
A time series is an organized collection of data that depicts the evolution of a
quantity through time. Pandas have a wide range of capabilities and tools for working
with time-series data in all fields.
Supported by pandas:
Analyzing time-series data from a variety of sources and formats.
Create time and date sequences with preset frequencies.
Date and time manipulation and conversion with timezone information.
A time series is resampled or converted to a specific frequency.
Calculating dates and times using absolute or relative time increments is one
way to.
13. Explain MultiIndexing in Pandas.
Multiple indexing is defined as essential indexing because it deals with data analysis
and manipulation, especially for working with higher dimensional data. It also
enables us to store and manipulate data with an arbitrary number of dimensions in
lower-dimensional data structures like Series and DataFrame.
14. How can we convert Series to DataFrame?
The conversion of Series to DataFrame is quite a simple process. All we need to do is
to use the to_frame() function.
Syntax:
Series.to_frame(name=None)
Parameters:
name: It accepts data objects as input. It is an optional parameter. The value of
the name parameter will be equal to the name of the Series if it has any.
Return Type: It returns the DataFrame a er converting it from Series.
15. How can we convert DataFrame to Numpy Array?
In order to convert DataFrame to a Numpy array we need to use
DataFrame.to_numpy() method.

Syntax:
DataFrame.to_numpy(dtype=None, copy=False, na_value=_NoDefault.no_default)
Parameters:
dtype: It accepts string or numpy.dtype. It is an optional parameter.
copy: It accepts a boolean value whose default is set to False.
na_value: It is an optional parameter. It specifies the value to use for missing
values. The data type will depend on the data type of the column in the
dataframe.
16. How can we convert DataFrame to an excel file?
In order to convert DataFrame to an excel file we need to use the to_excel()
function. There are various parameters to be considered. But initially, all you need is
to mention the DataFrame name and the name of the excel sheet.
Note: To write a single object to an Excel file, you need to provide the target file
name. However, if you want to write to multiple sheets, you must create an
ExcelWriter object that specifies the target file name and the sheet that needs to
be written. Alternatively, you can specify a unique sheet name to write multiple
sheets to the same Excel file.
Syntax:
data.to_excel( excel_writer, sheet_name='Sheet1', **kwargs )
Parameters:

excel_writer: It accepts a string or ExcelWriter object. It specifies the path of the
file to be written or an existing ExcelWriter object.
sheet_name: It accepts a string value. The default value is set to ‘Sheet1’. It
specifies the name of the sheet that will contain the DataFrame.
columns: It accepts a sequence or list of strings as input. It is an optional
parameter that specifies the columns that need to be written.
index: It accepts a boolean value whose default is set to True. It specifies the
rows/index to be written.
index_label: It accepts string or sequence of string values. It is an optional
parameter. It specifies the column label for index column(s) if required. If
nothing is specified, and the header and index are set to True, then the index
names are used. A sequence value should be given only if the DataFrame uses
MultiIndexing.
17. What is TimeDelta?
Timedeltas are diﬀerences in times, expressed in diﬀerent units, e.g. days, hours,
minutes, and seconds. They can be both positive and negative.
18. Explain Pandas Timedelta.seconds Property
Timedelta.seconds in pandas is used to return the number of seconds. Its
implementation is simpler than it sounds. We do not need any special parameters
and the return type is in the form of seconds.
Code Example :
#importing necessary libraries
import pandas as pd
import numpy as np
# Create the Timedelta object
td = pd.Timedelta('5 days 09:08:03.000000312')
# Print the Timedelta object
print(td)
# Print the Timedelta object in seconds format
print(td.seconds)

Output:-
5 days 09:08:03.000000312
32883
19. Is iterating over a Pandas Dataframe a good practice? If not
what are the important conditions to keep in mind before
iterating?
Ideally, iterating over pandas DataFrames is definitely not the best practice and one
should only consider doing so when it is absolutely necessary and no other function is
applicable. The iteration process through DataFrames is very ineﬀicient. Pandas
provide a lot of functions using which an operation can be executed without iterating
through the dataframe. There are certain conditions that need to be checked before
Before attempting to iterate through pandas objects, we must first ensure that none
of the below-stated conditions aligns with our use case:
Applying a function to rows: A common use case of iteration is when it comes
to applying a function to every row, which is designed to work only one row at a
time and cannot be applied on the full DataFrame or Series. In such cases, it’s
always recommended to use apply() method instead of iterating through the
pandas object.
Iterative manipulations: In case we need to perform iterative manipulations
and at the same time performance is a major area of concern, then we have
alternatives like numba and cython.
Printing a DataFrame: If we want to print out a DataFrame then instead of
iterating through the whole DataFrame we can simply use
DataFrame.to_string() method in order to render the DataFrame to a console-
friendly tabular output.
Vectorisation over iteration: It is always preferred to choose vectorization over
iteration as pandas come with a rich set of built-in methods whose performance
is highly optimized and super eﬀicient.

20. How would you iterate over rows in a DataFrame in Pandas?
Although it is not a good practice to iterate over rows in Pandas if there is no other
alternative we do so using either iterrows() or itertuples() built-in methods.
pandas.DataFrame.iterrows(): This method is used to iterate over DataFrame
rows as (index, Series) pairs. There is only one drawback for this method it does
not preserve the dtypes across rows due to the fact that it converts each row
into a Series. If you need to preserve the dtypes of the pandas object, then one
should use itertuples() method instead.
Code Example :
# import pandas package as pd
import pandas as pd
# Define a dictionary containing students data
data = {'Name': ['Sneha', 'Shreya',
'Sabhya', 'Riya'],
'Age': [22, 18, 10, 19],
'Stream': ['Computer', 'Commerce',
'Arts', 'Mechanical'],
'Percentage': [89, 93, 97, 73]}
# Convert the dictionary into DataFrame
df = pd.DataFrame(data, columns=['Name', 'Age',
'Stream', 'Percentage'])
print("Given Dataframe :n", df)
print("nIterating over rows using iterrows() method :n")
# iterate through each row and select
# 'Name' and 'Age' columns respectively.
for index, row in df.iterrows():
print(row["Name"], row["Age"])
Output:-

Given Dataframe :
Name Age Stream Percentage
0 Sneha 22 Computer 89
1 Shreya 18 Commerce 93
2 Sabhya 10 Arts 97
3 Riya 19 Mechanical 73
Iterating over rows using iterrows() method :
Sneha 22
Shreya 18
Sabhya 10
Riya 19
pandas.DataFrame.itertuples(): This method is used to iterate over DataFrame
rows as namedtuples. Also, itertuples() are faster than compared to
iterrows() .
Code Example :
print("nIterating over rows using itertuples() method :n")
# iterate through each row and select
# 'Name' and 'Percentage' column respectively.
for row in df.itertuples(index=True, name='Pandas'):
print(getattr(row, "Name"), getattr(row, "Percentage"))
Output:-
Iterating over rows using itertuples() method :
Sneha 89
Shreya 93
Sabhya 97
Riya 73
21. List some statistical functions in Python Pandas?
Some of the major statistical functions in Python Pandas are:

sum() – It returns the sum of the values.
min() – It returns the minimum value.
max() – It returns the maximum value.
abs() – It returns the absolute value.
mean() – It returns the mean which is the average of the values.
std() – It returns the standard deviation of the numerical columns.
prod() – It returns the product of the values.
22. How to Read Text Files with Pandas?
There are multiple ways in which we read a text file using Pandas.
Using read_csv(): CSV is a comma-separated file i.e. any text file that uses
commas as a delimiter to separate the record values for each field. Therefore, in
order to load data from a text file we use pandas.read_csv() method.
Using read_table(): This function is very much like the read_csv() function,
the major difference being that in read_table the delimiter value is ‘ t ’ and
not a comma which is the default value for read_csv() . We will read data with
the read_table function making the separator equal to a single space(‘ ‘).
Using read_fwf(): It stands for fixed-width lines. This function is used to load
DataFrames from files. Another very interesting feature is that it supports
optionally iterating or breaking the file into chunks. Since the columns in the
text file were separated with a fixed width, this read_fwf() read the contents
effectively into separate columns.
23. How are iloc() and loc() different?
DataFrame.iloc(): It is a method used to retrieve data from a Data frame, and it
is an integer position-based locator (from 0 to length-1 of the axis), but
may also be used with a boolean array and this is the major difference factor
between iloc() and loc() . It takes input as integers, arrays of integers, an
object, boolean arrays, and functions.
Code Example :

Output:-
Name Age Marks
0 Kate 10 85
2 Sheila 12 91
DataFrame.loc(): It gets rows or columns with particular labels as input. It takes
input as a single label, a list of arrays, and objects with labels. It does not work
with boolean arrays or values.
Code Example :
Output:-
Name Age Marks
0 Kate 10 85
1 Harry 14 77
2 Sheila 12 91
Name Age Marks
0 Kate 10 85
24. How will you sort a DataFrame?
import pandas as pd
student_dict = {'Name': ['Kate', 'Harry', 'Sheila'], 'Age': [10, 14, 12], 'Marks': [85,
# create DataFrame from dict
df = pd.DataFrame(student_dict)
print(df)
print(df.iloc[[0, 2]])
import pandas as pd
print(df)
print(df.loc[(df.Name=='Kate')])

The function used for sorting in pandas is called DataFrame.sort_values() . It is used
to sort a DataFrame by its column or row values. The function comes with a lot of
parameters, but the most important ones to consider for sort are:
by: It is used to specify the column/row(s) which are used to determine the
sorted order. It is an optional parameter.
axis: It specifies whether the sorting is to be performed for a row or column and
the value is 0 and 1 respectively.
ascending: It specifies whether to sort the dataframe in ascending or
descending order. The default value is set to ascending. If the value is set as
ascending=False it will sort in descending order.
25. How would you convert continuous values into discrete
values in Pandas?
Depending on the problem, continuous values can be discretized using the cut()
or qcut() function:
cut() It bins the data based on values. We use it when we need to segment and
sort data values into bins that are evenly spaced. cut() will choose the bins to
be evenly spaced based on the values themselves and not the frequency of those
values. For example, cut could convert ages to groups of age ranges.
qcut() bins the data based on sample quantiles. We use it when we want to have
the same number of records in each bin or simply study the data by quantiles.
For example, if in a data we have 30 records, and we want to compute the
quintiles, qcut() will divide the data such that we have 6 records in each
bin.
26. What is the diﬀerence between join() and merge() in
Pandas?
Both join and merge functions are used to combine two dataframes. The major
diﬀerence is that the join method combines two dataframes on the basis of their
indexes whereas the merge method is more flexible and allows us to specify columns
along with the index to combine the two dataframes.

These are the main differences between df.join() and df.merge() :
lookup on right table: When performing a lookup on the right table, the
join() method will always use the index of df2 to perform the join
operation. However, if you use the merge() method, you can choose to join
based on one or more columns of df2 by default, or even the index of df2
if you specify the right_index=True parameter.
lookup on le table: When performing a lookup on the le table,
df1.join(df2) method will use the index of df1 by default, while
df1.merge(df2) method will use the column(s) of df1 for the join operation.
However, you can override this behavior by specifying the on=key_or_keys
parameter in df1.join(df2) or by setting the left_index=True parameter in
df1.merge(df2) .
le vs inner join: By default, the df1.join(df2) method performs a le join
(retains all rows of df1 ), while the df1.merge(df2) method performs an
inner join (returns only the matching rows of df1 and df2 ).
27. What is the difference(s) between merge() and concat() in
Pandas?
Both concat and merge functions are used to combine dataframes. There are three
major key differences between these two functions.

The Way of Combining: concat() function concatenates dataframes along rows
or columns. It is nothing but stacking up of multiple dataframes whereas
merge() combines dataframes based on values in shared columns thus it is
more flexible compared to concat() as the combination can happen based on
the given condition.
Axis parameter: concat() function has axis parameter. Since merge()
function combines dataframes on the basis of shared columns side by side it
does not really need an axis parameter. The value of the axis parameter decides
in what direction will the concatenation happen. For it to happen row-wise the
value of the axis parameter will be ‘0’ and for it to happen side-by-side it will be
‘1’. The default value is 1.
Join vs How: Join is a parameter of concat() function and how is a parameter
of merge() function. Join can take two values outer and inner whereas how
can take four values inner, outer, le , and right.
28. What’s the diﬀerence between interpolate() and fillna() in
Pandas?
fillna(): It fills the NaN values with a given number with which you want to
substitute. It gives you the option to fill according to the index of rows of a
pd.DataFrame or on the name of the columns in the form of a python dict.
Code Example :
import pandas as pd
import numpy as np
#Creating a dataframe
df = pd.DataFrame({"Value_1":[None, 14, 35, None, 1,12, 74, 65, None, 1],
"Value_2":[None, 24, 54, 3, None,None, 2, 54, 3, None],
"Value_3":[20, 16, None, 3, 8,None, 2, 54, 3, None],
"Value_4":[None, 2, 54, 3, None,14, 3, None, None, 6]})
print(df)
#Fill the missing value with average values in column Value1.
df['Value_1'].fillna(int(df13['Value_1'].mean()), inplace = True)
df.head()

Output:
Value_1 Value_2 Value_3 Value_4
0 NaN NaN 20.0 NaN
1 14.0 24.0 16.0 2.0
2 35.0 54.0 NaN 54.0
3 NaN 3.0 3.0 3.0
4 1.0 NaN 8.0 NaN
5 12.0 NaN NaN 14.0
6 74.0 2.0 2.0 3.0
7 65.0 54.0 54.0 NaN
8 NaN 3.0 3.0 NaN
9 1.0 NaN NaN 6.0
# after using fillna method.
Value_1 Value_2 Value_3 Value_4
0 28 <NA> 20 <NA>
1 14 24 16 2
2 35 54 15 54
3 28 3 3 3
4 1 <NA> 8 <NA>
interpolate(): It gives you the flexibility to fill the missing values with many
kinds of interpolations between the values like linear, time, etc.
Code Example :
import pandas as pd, numpy as np
df = pd.Series([1, np.nan, np.nan, 3])
print(df.interpolate())
Output:-
0 1.000000
1 1.666667
2 2.333333
3 3.000000
dtype: float64
29. How to set Index to a Pandas DataFrame?

Changing Index column: In this example, the First Name column has been made
the index column of DataFrame.
Code Example :
Output:
Name Age Marks
0 Kate 10 85
1 Harry 11 77
2 Sheila 12 91
Age Marks
Name
Kate 10 85
Harry 11 77
Sheila 12 91
Set Index using Multiple Column: In this example, two columns will be made as
an index column. The drop parameter is used to Drop the column and the
append parameter is used to append passed columns to the already existing
index column.
Code Example :
import pandas as pd
student_df = pd.DataFrame(student_dict)
print(student_df)
# set index using column
student_df = student_df.set_index('Name')
print(student_df)

Output:
Name Age Marks
0 Kate 10 85
1 Harry 11 77
2 Sheila 12 91
Age
Name Marks
Kate 85 10
Harry 77 11
Sheila 91 12
Set index using a List:
Code Example :
index = pd.Index(['x1', 'x2', 'x3'])
student_df = student_df.set_index(index)
print(student_df)
Output:
Age Marks
x1 10 85
x2 11 77
x3 12 91
Set multi-index using a list and column
Code Example :
import pandas as pd
print(student_df)
student_df = student_df.set_index(['Name', 'Marks'])
print(student_df)

Output:-
Name Age Marks
0 Kate 10 85
1 Harry 11 77
2 Sheila 12 91
Age Marks
Name
x1 Kate 10 85
x2 Harry 11 77
x3 Sheila 12 91
30. How to add a row to a Pandas DataFrame?
We can add a single row using DataFrame.loc: We can add the row at the last in our
dataframe. We can get the number of rows using len( DataFrame.index ) for
determining the position at which we need to add the new row.
Code Example :
Output:
import pandas as pd
print(student_df)
index = pd.Index(['x1', 'x2', 'x3'])
student_df = student_df.set_index([index, 'Name'])
print(student_df)
# Add row
import pandas as pd
print(student_df)
student_df.loc[len(student_df.index)] = ['Alex', 19, 93]
print(student_df)

Name Age Marks
0 Kate 10 85
1 Harry 11 77
2 Sheila 12 91
Name Age Marks
0 Kate 10 85
1 Harry 11 77
2 Sheila 12 91
3 Alex 19 93
We can also add a new row using the DataFrame.append() function:
Code Example :
df2 = {'Name': 'Tom', 'Age': 18, 'Marks': 73}
student_df = student_df.append(df2, ignore_index = True)
print(student_df)
Output:
Name Age Marks
0 Kate 10 85
1 Harry 11 77
2 Sheila 12 91
3 Alex 19 93
4 Tom 18 73
We can also add multiple rows using the pandas.concat(): by creating a new
dataframe of all the rows that we need to add and then appending this dataframe to
the original dataframe.
Code Example :

Output:
Name Age Marks
0 Kate 10 85
1 Harry 14 77
2 Sheila 12 91
Name Age Marks
0 Amy 19 93
1 Maddy 12 81
Name Age Marks
0 Harry 14 77
1 Sheila 12 91
2 Amy 19 93
3 Maddy 12 81
31. How to add a column to a Pandas DataFrame?
We first create the dataframe and then look into the various methods one by one.
Code Example :
import pandas as pd
print(df)
dict = {'Name':['Amy', 'Maddy'],
'Age':[19, 12],
'Marks':[93, 81]
}
df2 = pd.DataFrame(dict)
print(df2)
df3 = pd.concat([df1, df2], ignore_index = True)
print(df3)

Output:-
Name Age Marks
0 Kate 10 85
1 Harry 11 77
2 Sheila 12 91
Name Age Marks Address
0 Kate 10 85 Chicago
1 Harry 11 77 London
2 Sheila 12 91 Berlin
By declaring a new list as a column.
Code Example :
Output:-
# Add column
import pandas as pd
print(student_df)
address = ['Chicago', 'London', 'Berlin']
student_df['Address'] = address
# Observe the result
print(student_df)
# Add column
import pandas as pd
print(student_df)
print(student_df)

Name Age Marks
0 Kate 10 85
1 Harry 11 77
2 Sheila 12 91
By using DataFrame.insert(): It gives the freedom to add a column at any
position we like and not just at the end. It also provides diﬀerent options for
inserting the column values.
Code Example :
Output:-
Name Age Marks
0 Kate 10 85
1 Harry 11 77
2 Sheila 12 91
Name Age Address Marks
0 Kate 10 Chicago 85
1 Harry 11 London 77
2 Sheila 12 Berlin 91
Using Dataframe.assign() method: This method will create a new dataframe
with a new column added to the old dataframe.
Code Example :
import pandas as pd
print(student_df)
student_df.insert(2, "Address", ['Chicago', 'London', 'Berlin'], True)
print(student_df)

Output:-
Name Age Marks
0 Kate 10 85
1 Harry 11 77
2 Sheila 12 91
Name Age Marks address
By using a dictionary: We can use a Python dictionary to add a new column in
pandas DataFrame. Use an existing column as the key values and their
respective values will be the values for a new column.
Code Example :
import pandas as pd
print(student_df)
df2 = student_df.assign(address=['Chicago', 'London', 'Berlin'])
print(df2)
import pandas as pd
print(student_df)
address = {'Chicago': 'Kate', 'London': 'Harry',
'berlin': 'Sheila'}
# Observe the output
print(student_df)

Output:-
Name Age Marks
0 Kate 10 85
1 Harry 11 77
2 Sheila 12 91
2 Sheila 12 91 berlin
32. How can we convert DataFrame into a NumPy array?
In order to convert a dataframe into a NumPy array we use DataFrame.to_numpy()
method.
Syntax:
DataFrame.to_numpy(dtype=None, copy=False, na_value=_NoDefault.no_default)
Parameters:
dtype: It accepts string or numpy.dtype value. It is an optional parameter.
copy: It accepts a boolean value. The default value is set to False.
It ensures that the returned value is not a view on another array. Setting the
value of copy=False does not ensure that to_numpy() is no-copy. Whereas if
copy=True it does ensure that a copy is made.
na_value: It accepts the parameter of any datatype and it is an optional
parameter. It specifies the value to be used for missing values. The default value
is of the same data type as the object.
Code Example :

import pandas as pd
# initialize a dataframe
df = pd.DataFrame(
[[10, 12, 33],
[41, 53, 66],
[17, 81, 19],
[10, 11, 12]],
columns=['X', 'Y', 'Z'])
# convert dataframe to numpy array
arr = df.to_numpy()
print('nNumpy Arrayn----------n', arr)
print(type(arr))
Output:
Numpy Array
----------
[[ 1 2 3]
[ 4 5 6]
[ 7 8 9]
[10 11 12]]
<class 'numpy.ndarray'>
33. How will you compute the percentile of a numerical series in
Pandas?
In order to compute percentile we use numpy.percentile() method.
Syntax:
It will calculate the q-th percentile of the given the data along the mentioned axis.
Parameters:
a: It is an input array or object that can be converted to an array.
q: It is the percentile or sequence of percentiles to be calculated. The value must
be between 0 and 100 both inclusive.
numpy.percentile(a, q, axis=None, out=None, overwrite_input=False, method='linear', kee

Code Example :
import pandas as pd
import random
A = [ random.randint(0,100) for i in range(10) ]
B = [ random.randint(0,100) for i in range(10) ]
df = pd.DataFrame({ 'field_A': A, 'field_B': B })
df
print(df.field_A.quantile(0.1)) # 10th percentile
print(df.field_A.quantile(0.5)) # same as median
print(df.field_A.quantile(0.9)) # 90th percentile
Output:-
12.1
52.0
92.6
34. How to create Timedelta objects in Pandas?
String: In order to create a timedelta object using a string argument we pass a string
literal.
Code Example :
import pandas as pd
import numpy as np
#Conversion from string format to date format takes place using Timedelta method.
print (pd.Timedelta('20 days 12 hours 45 minutes 3 seconds'))
Output:

20 days 12:45:03
*Integer: What differs from string, in this case, is we just need to pass an integer value
and the object will be created.
Code Example :
import pandas as pd
import numpy as np
print (pd.Timedelta(16,unit='h'))#h here is used for hours.
Output:
0 days 16:00:00
Data Offsets: In order to first learn how to create a timedelta object using data offset
we first need to understand what data offset actually is. Data offsets are parameters
like weeks, days, hours, minutes, seconds, milliseconds, microseconds, and
nanoseconds. This when passed as an argument helps in the creation of the
timedelta object.
Code Example :
import pandas as pd
import numpy as np
print (pd.Timedelta(days=2, hours = 16))
Output:-
2 days 16:00:00
Code Example :

import pandas as pd
import numpy as np
print (pd.Timedelta(days=2, hours = 6, minutes = 23))
Output:-
2 days 06:23:00
35. How do you split a DataFrame according to a boolean
criterion?
We can create a mask to separate the dataframe and then use the inverse operator
( ~ ) to take the complement of the mask.
Code Example :
Output:-
Name Age Marks
0 Kate 10 85
1 Harry 14 77
2 Sheila 12 91
Name Age Marks
1 Harry 14 77
2 Sheila 12 91
import pandas as pd
print(df)
df1 = df[df['Age'] > 10]
# printing df1
df1

36. How can we convert NumPy array into a DataFrame?
In order to convert a Numpy array into a DataFrame we first need to create a numpy
array and then use the pandas.DataFrame the method along with specifying
the/labels for rows and columns.
Code Example :
# Python program to Create a
# Pandas DataFrame from a Numpy
# array and specify the index
# column and column headers
# import required libraries
import numpy as np
import pandas as pd
# creating a numpy array
numpyArray = np.array([[115, 222, 343],
[323, 242, 356]])
# generating the Pandas dataframe
# from the Numpy array and specifying
# name of index and columns
dataframe = pd.DataFrame(data = numpyArray,
index = ["Row1", "Row2"],
columns = ["Column1",
"Column2", "Column3"])
# printing the dataframe
print(dataframe)
Output:-
Column1 Column2 Column3
Row1 115 222 343
Row2 323 242 356
37. How to delete a row in Pandas DataFrame?

The drop() method is used to delete a row in a DataFrame. If we set the value of
the axis parameter as ‘ 0 ’ or do not mention it at all it will work for rows as the
default value for the axis parameter is set to ‘ 0 ’, if we set the value to ‘ 1 ’ it will
delete the column in the DataFrame.
Code Example :
Output:-
Age Marks
Name
Kate 10 85
Harry 11 77
Sheila 12 91
Age Marks
Name
Kate 10 85
Sheila 12 91
38. How to delete a column in Pandas DataFrame?
The drop() method is used to delete a column in a DataFrame. If we set the value
of the axis parameter as ‘1’ it will work for a column if we set the value to ‘0’ it will
delete the rows in the DataFrame.
Code Example :
import pandas as pd
print(student_df)
student_df.drop(["Harry"], inplace = True)
print(student_df)

Output:-
Name Marks
0 Kate 85
1 Harry 77
2 Sheila 91
39. How to get frequency count of unique items in a Pandas
DataFrame?
In order to get the frequency count of unique items in a Pandas DataFrame we can
use the Series.value_counts() method.
Code Example :
# importing the module
import pandas as pd
# creating the series
s = pd.Series(data = [1,2,3,4,3,5,3,7,1])
# displaying the series
print(s)
# finding the unique count
print(s.value_counts())
Output:-
import pandas as pd
student_df.drop(["Age"], axis = 1, inplace = True)
print(student_df)

0 1
1 2
2 3
3 4
4 3
5 5
6 3
7 7
8 1
dtype: int64
3 3
1 2
2 1
4 1
5 1
7 1
dtype: int64
40. How to rename the index in a Pandas DataFrame?
In order to rename a DataFrame we use the Dataframe.set_index() method to give
diﬀerent values to the columns or the index values of DataFrame. Like in this
example we will change the index label from ‘Name’ to ‘FirstName’.
Code Example :
Output:-
import pandas as pd
print(student_df)
student_df.index.names = ['FirstName']
print(student_df)

Age Marks
Name
Kate 10 85
Harry 11 77
Sheila 12 91
Age Marks
FirstName
Kate 10 85
Harry 11 77
Sheila 12 91
41. How to reset the index in a Python Pandas DataFrame?
Inorder to reset the index of the DataFrame we use the Dataframe.reset_index()
command. If the DataFrame has a MultiIndex, this method can also remove one or
more levels.
Code Example :
Output:-
import pandas as pd
print(student_df)
print(student_df)
student_df.reset_index(drop=True, inplace=True)
print(student_df)

Name Age Marks
0 Kate 10 85
1 Harry 11 77
2 Sheila 12 91
Age Marks
Name
Kate 10 85
Harry 11 77
Sheila 12 91
Age Marks
0 10 85
1 11 77
2 12 91
Conclusion
In this article, we have seen commonly asked pandas interview questions. These
questions along with regular problem practice sessions will help you crack any
pandas-based interview. We divided the article into four sections:
The BASIC python pandas interview questions section contains questions based
on theoretical concepts covering diﬀerent segments like Data Structures in
Pandas, Time Series, Statistical methods, etc.
The ADVANCED python pandas interview questions section delves a little deeper
into the conceptual section covering various methods like join() , merge() ,
groupby() , their functionalities, implementation, etc.
The DATA SCIENCE python pandas interview questions section focuses on
application-based questions the ones that a data scientist might face during his
day-to-day work like formatting a dataframe, or working on data aggregation,
etc.
The CODING python pandas interview section focuses on questions that test the
python coding skills along with the general concepts involved. You might be
asked to write a code to calculate percentile or to convert a Numpy array to a
DataFrame or vice-versa, etc.

Along with theoretical knowledge of pandas, there is an emphasis on the ability to
write good-quality code as well. So keep learning and practising problems you’ll no
doubt succeed at any pandas interview.
42. How can you find the row for which the value of a specific
column is max or min?
We can find the row for which the value of a specific column is by using idxmax and
idxmin functions.
Code Example :
import pandas as pd
data = {
"sales": [23, 34, 56],
"age": [50, 40, 30]
}
print(df.idxmax())
print(df.idxmin())
Output:-
sales 2
age 0
dtype: int64
sales 0
age 2
dtype: int64
43. Explain the GroupBy function in Pandas

Python pandas Dataframe.groupby() function is used for grouping the data
according to the categories and applying a function to those categories. It helps in
data aggregation in an eﬀicient manner. It splits the data into groups based on some
given criteria. The pandas objects can be split on any of their axes. In brief
groupby() provides the mapping of labels to their respective group names.
Syntax:
Code Example :
Output:-
Name Age Marks
0 Kate 10 85
1 Harry 14 77
2 Sheila 12 91
Name Marks
Age
10 Kate 85
12 Sheila 91
14 Harry 77
44. How to format data in your Pandas DataFrame?
DataFrame.groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=_No
import pandas as pd
print(df)
gk = df.groupby('Age')
gk.first()

When we start working on a dataset or a set of data we need to perform some
operations on the values in the DataFrame. At times these values might not be in the
right format for you to work on it thus formatting of data is required. There are
multiple ways in which we can format data in a Pandas DataFrame.
One way is by Replacing All Occurrences of a String in a DataFrame. In order to
replace Strings in our DataFrame, we can use replace() method i.e. all we
need is to pass the values that we would like to change, followed by the values
we want to replace them with.
One other way is by Removing Parts From Strings in the Cells of the
DataFrame. Removing unwanted parts of strings is cumbersome work. Luckily,
there is a solution in place! We can do it easily by using map() function on the
column result to apply the lambda function over each element or element-wise
of the column.
Splitting Text in a Column into Multiple Rows in a DataFrame. The process of
splitting text into multiple rows is quite a complex task. We can do so by
applying a function to the Pandas DataFrame’s Columns or Rows.
45. What is the use of pandas.Dataframe.aggregate() function?
Explain its syntax and parameters.
Data Aggregation is defined as the process of applying some aggregation function to
one or more columns. It uses the following:
sum: It is used to return the sum of the values for the requested axis.
min: It is used to return a minimum of the values for the requested axis.
max: It is used to return maximum values for the requested axis.
Its Syntax is:
DataFrame.aggregate(func=None, axis=0, *args, **kwargs)
Aggregate using one or more operations over the specified axis.
Parameters:

func: It takes string, list, dictionary, or function values as input. It represents the
function to use for data aggregation.
axis: It takes in only two values ‘0’ or ‘1’. 0 is for the index and 1 is for columns.
If 0 or ‘index’: The function is applied to each column.
If 1 or ‘columns’: The function is applied to each row.
The default value is set to 0.
It returns the aggregated dataframe as the output.
Code Example :
import pandas as pd
data = {
"x": [560, 240, 630],
"y": [300, 1112, 452]
}
x = df.aggregate(["sum"])
y = df.aggregate(["min"])
z = df.aggregate(["max"])
print(x)
print(y)
print(z)
Output:
x y
sum 1430 1864
x y
min 240 300
x y
max 630 1112
46. How to get items of series A not present in series B?
In order to find items from series A that are not present in series B by using the
isin() method combining it with the Bitwise NOT operator in pandas. We can
understand this using a code example:

Code Example :
# Importing pandas library
import pandas as pd
# Creating 2 pandas Series
series1 = pd.Series([12, 24, 38, 210, 110, 147, 929])
series2 = pd.Series([17, 83, 76, 54, 110, 929, 510])
print("Series1:")
print(series1)
print("nSeries2:")
print(series2)
# Using Bitwise NOT operator along
# with pandas.isin()
print("nItems of series1 not present in series2:")
res = series1[~series1.isin(series2)]
print(res)
Output:-

Series1:
0 12
1 24
2 38
3 210
4 110
5 147
6 929
dtype: int64
Series2:
0 17
1 83
2 76
3 54
4 110
5 929
6 510
dtype: int64
Items of series1 not present in series2:
0 12
1 24
2 38
3 210
5 147
dtype: int64
47. Describe a few data operations in Pandas.
There are several useful data operations for DataFrame in Pandas, which are as
follows:

1. String Operation: Pandas provide a set of string functions for working with
string data. The following are the few operations on string data:
lower(): Any strings in the index or series are converted to lowercase
letters.
upper(): Any strings in the index or series are converted to uppercase
letters.
strip(): This method eliminates spacing from every string in the
Series/index, along with a new line.
islower(): If all of the characters in the Series/Index string are lowercase, it
returns True. Otherwise, False is returned.
isupper(): If all of the characters in the Series/Index string are uppercase, it
returns True. Otherwise, False is returned.
split(’ '): It’s a method that separates a string according to a pattern.
cat(sep=’ '): With a defined separator, it concatenates series/index items.
contains(pattern): If a substring is available in the current element, it
returns True; otherwise, it returns False.
replace(a,b): It substitutes the value b for the value a.
startswith(pattern): If all of the components in the series begin with a
pattern, it returns True.
endswith(pattern): If all of the components in the series terminate in a
pattern, it returns True.
find(pattern): It can be used to return the pattern’s first occurrence.
findall(pattern): It gives you a list of all the times the pattern appears.
swapcase: It is used to switch the lower/upper case.
2. Null values: When no data is being sent to the items, a Null value/missing value
can appear. There may be no values in the respective columns, which are
commonly represented as NaN. Pandas provide several useful functions for
identifying, deleting, and changing null values in Data Frames. The following are
the functions.
isnull(): isnull 's job is to return true if either of the rows has null values.
notnull(): It is the inverse of the isnull() function, returning true values for
non-null values.
dropna(): This function evaluates and removes null values from rows and
columns.
fillna(): It enables users to substitute other values for the NaN values.
replace(): It’s a powerful function that can take the role of a regex,
dictionary, string, series, and more.

48. Compare the Pandas methods: map(), applymap(), apply()
The map() method is an elementwise method for only Pandas Series, it maps
values of the Series according to input correspondence.
It accepts dicts, Series, or callable. Values that are not found in the dict are converted
to NaN.
Code Example: We first create a dataframe and then apply the respective methods to
it.
import pandas as pd
# Series generation
str_string = 'scalar'
str_series = pd.Series(list(str_string))
print("Original seriesn" +
str_series.to_string(index=False,
header=False), end='nn')
# Using apply method for converting characters
# present in the original series
new_str_series = str_series.map(str.upper)
print("Transformed series:n" +
new_str_series.to_string(index=False,
Output:
Original series
s
c
a
l
a
r
Transformed series:
S
C
A
L
A
R

The applymap() method is an elementwise function for only DataFrames, it
applies a function that accepts and returns a scalar to every element of a
DataFrame.
It accepts callables only i.e. a Python function.
Code Example :
import pandas as pd
df = pd.DataFrame(
[['a', 'b', 'c'],
['d','e','f'],
],
print(df)
new_df = df.applymap(str.upper)
print("Transformed dataframe:n" +
new_df.to_string(index=False,
Output:-
X Y Z
0 a b c
1 d e f
Transformed dataframe:
A B
D E F
The apply() method also works elementwise, as it applies a function along the
input axis of DataFrame. It is suited to more complex operations and
aggregation.
It accepts the callables parameter as well.
Code Example :

import pandas as pd
df = pd.DataFrame(
[[10, 12, 33],
[41, 53, 66],
[17, 81, 19],
[10, 11, 12]],
print(df)
new_df = df.apply(lambda x:x.sort_values(), axis = 1)
print("Transformed dataframe:n" +
new_df.to_string(index = False,
header = False), end = 'nn')
Output:-
X Y Z
0 10 12 33
1 41 53 66
2 17 81 19
3 10 11 12
Transformed dataframe:
10 12 33
41 53 66
17 81 19
10 11 12
49. What’s the diﬀerence between pivot_table() and groupby()?
Both pivot_table() and groupby() are used to aggregate your dataframe. The
major diﬀerence is in the shape of the result.
Code Example:

Output:-
Name Age Marks
0 Kate 10 85
1 Harry 14 77
2 Sheila 12 91
Marks
Name Age
Harry 14 77
Kate 10 85
Sheila 12 91
Code Example :
Output:
import pandas as pd
print(df)
table = pd.pivot_table(df, index =['Name', 'Age'])
print(table)
import pandas as pd
print(df)
gk = df.groupby('Age')
gk.first()

Name Age Marks
0 Kate 10 85
1 Harry 14 77
2 Sheila 12 91
Age Name Marks
10 Kate 85
12 Sheila 91
14 Harry 77
50. When to use merge() over concat() and vice-versa in Pandas?
The use of concat() function comes into play when combining homogeneous
DataFrame, while the merge() function is considered first when combining
complementary DataFrame.
If we need to merge vertically, we should always use pandas.concat() . whereas if
need to merge horizontally via columns, we should go with pandas.merge() , which
by default merges on the columns that are in common between the dataframes.
Code Example :
df1 = pd.DataFrame({'Key': ['b', 'b', 'a', 'c'], 'data1': range(4)})
df2 = pd.DataFrame({'Key': ['a', 'b', 'd'], 'data2': range(3)})
#Merge
# The 2 dataframes are merged on the basis of values in column "Key" as it is
# a common column in 2 dataframes
print(pd.merge(df1, df2))
#Concat
# df2 dataframe is appended at the bottom of df1
print(pd.concat([df1, df2]))
Output:-

Key data1 data2
0 b 0 1
1 b 1 1
2 a 2 0
Key data1 data2
0 b 0.0 NaN
1 b 1.0 NaN
2 a 2.0 NaN
3 c 3.0 NaN
0 a NaN 0.0
1 b NaN 1.0
2 d NaN 2.0

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