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Top Pandas Interview Questions and Answers (2024)

Author Manish Kumar
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Introduction

Pandas is software that helps people analyze and change data. It has lots of tools and things to use that can help scientists and engineers work with data. This article talks about some common questions people might get asked about Pandas in a job interview. We divided the questions into Easy, Medium, and Hard sections so that it's easier to understand. Our goal is to help you be ready to answer any Pandas questions you might get asked in an interview.

Top Pandas Interview Questions

Pandas Interview Questions and Answers for Freshers

This section will get the basic pandas interview questions to build a solid foundation. This section is crucial since it establishes a strong base. 

1.  Explain Python Pandas?

Ans: Python Pandas is a data analysis and manipulation software library built by Wes McKinney. It is an open-source, cross-platform library. It provides data structures and procedures for numerical and time series data manipulation. It makes machine learning algorithms easy to implement.

2. What is the use of Python Pandas?

Ans: It is used for data analysis, time series manipulation, and table management. It is specially designed for the Python programming language.

3. Define series in Pandas?

Ans: It is a one-dimensional array of objects of any data type. Using the 'series'

method, you can convert any list, tuple, and dictionary into a series. A series cannot have a column. The row labels of the series are called indexes.

4. What are the types of data structures available in Pandas?

Ans: Pandas provides two types of data structures built on top of NumPy. These are

  • series and DataFrames.
  • Series are one-dimensional, whereas DataFrames are two-dimensional data types.

5. What are the critical features of Pandas?

Ans: The features of Pandas library are:

  • Time Series
  • Data Alignment
  • Merge and Join
  • Reshaping
  • Memory efficient

6.How can the standard deviation be calculated from the Series?

Ans: In pandas, you can calculate the standard deviation of a Series using the .std() method. For example:

import pandas as pd
data = pd.Series([1, 2, 3, 4, 5])
std_deviation = data.std()


Here, std_deviation will contain the standard deviation of the data in the Series.

7. Define DataFrames in Pandas?

Ans: A DataFrame is an extensively used data structure in Pandas and works with 2-D arrays with labelled axes. It is a standard storing data with row and column indices. The columns can store heterogeneous data such as int and bool. It can be viewed as a dictionary of series data structures.
 

8. What is the time series in Pandas?

Ans: Time series is an organised collection of data points showing a quantity's evolution over time. Pandas are extremely capable and have the tools to work with time series data from various fields.
Functions provided by Pandas:

  • Create date and time sequences using preset frequencies
  • Date and time manipulation supported by timezone feature
  • Conversion of time series to a given frequency or to resample 
  • Analysing time series data from several sources
  • Calculating date and time in absolute or relative terms 

9. Explain reindexing in Pandas?

Ans: Reindexing allows the assignment of new indices and has configurable filling logic. It injects NA/NaN in the areas where the elements are missing from the last index. It returns an object unless the new index is equivalent to the current one, and the value of the copy becomes false. It is used to alter the index of the rows and columns of the DataFrame.

10.  Explain MultiIndexing in Pandas.

Ans: MultiIndexing in Pandas allows us to have multi-levels of row and column labels which provide a way to analyze and represent data. With the help of MultiIndexing, one can organize the data in a tabular format with multiple features.

11.  What is TimeDelta?

Ans: TimeDelta is a data type in Python. It represents the duration or difference between two points in time. TimeDelta is mainly used to perform arithmetic operations involving dates and times. It can be positive or negative and can store values for days, seconds, minutes, hours, and weeks.

12. How to create a series from a dictionary in Pandas?

Ans: The Series() method is used without the index parameter to create a series.

13.  Which library tool is used to create a scatter plot matrix?

Ans: Scatter_matrix is used for this purpose.

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Pandas Interview Questions and Answers for Intermediate

We discussed some of the easy-level Pandas Interview Questions. Let us now go through some of the medium-level Pandas Interview Questions. 

14. What is Time Offset?

Ans: A pandas Series or DataFrame can be shifted or offset by using a time offset, which is a relative period of time. The representation of time spans like days, weeks, months, and years can be done using time offsets.

The pandas.offsets module can be used to produce time offsets. A range of pre-defined time offsets, including Day(), Week(), Month(), and Year(), are available in the pandas.offsets module. By mixing the pre-defined time offsets, you can also produce custom time offsets.

15. Explain Categorical data in Pandas?

Ans: A Categorical data is a Pandas data type corresponding to a categorical variable in statistics. A categorical variable usually takes a limited and fixed number of values. All values of categorical data are in categories, and np. Nan.

16. How to create a copy of the series in Pandas?

Ans: To create a copy of the series, use the following code snippet:
             pandas.Series.copy

Series.copy(deep=True)
The above code creates a deep copy that includes a copy of data and indices. It will not copy data or indices if deep is set to false.

17. How to add an index in DataFrames?

Ans: While creating a DataFrame, you can add inputs to the index argument. It will ensure that you have the required index. If you don't specify inputs, the DataFrame, by default, contains a numerical index that starts with zero and ends on the last row of the DataFrame.

18. How to delete an index from DataFrame?

Ans: First, reset the index of DataFrame and then execute the following command to remove the index name.
del df.index.name
Remove duplicate index values and drop the identical values from the index column.

19. How can I remove indices, rows, or columns from a Pandas DataFrame?

Ans: Use the drop() method to eliminate indices, rows, or columns from a Pandas DataFrame. The DataFrame's related indices, rows, or columns are eliminated by the drop() method, which accepts a list of labels as an input.

20. How to rename an index or column in a DataFrame?

Ans: We can use the .rename method to change the index and columns name.

21. How to add a column to DataFrame?

Ans: You can add new columns to the existing DataFrame. Follow the code snippet below to add a column :

#CODING NINJAS
# importing the pandas library as pd   
import pandas as pd      
info = {'one' : pd.Series([21, 12, 33, 14, 51], index=['a', 'b', 'c', 'd', 'e']),    
            'two' : pd.Series([18, 32, 39, 48, 45, 56], index=['a', 'b', 'c', 'd', 'e', 'f'])}
info = pd.DataFrame(info)            
print ("Passing a series to add new column")    
info['three']=pd.Series([89,65,67],index=['a','b','c'])    
print (info)    
print ("Add new column using previous columns")    
info['four']=info['one']+info['three']    
print (info)

22. How to add rows to a DataFrame?

Ans: You can use .loc, iloc and ix to add new rows to a DataFrame.
loc work for labels of the index, iloc works for the position and ix requires a label to be passed to it if it is integer based.

23. How to iterate over a Pandas DataFrame?

Ans: To iterate over the rows of the DataFrame, use loop with the iterrows() method.

24. What is Pandas NumPy array?

Ans: NumPy extends to Numerical Python. Calculations in NumPy arrays are faster than in regular Python arrays. It is a Python package to perform various analyses and process single-dimensional and multidimensional array elements.

25. How to convert DataFrame to NumPy array?

Ans: DataFrames can be converted to NumPy arrays to perform high-level mathematical computations. You can use DataFrame.to_numpy() method for conversion. This function will return a NumPy array.

26. List some statistical functions in Python Pandas?

Ans: Below are some statistical functions in Python Pandas.

  • mean(): This function computes the arithmetic mean along a specified axis.
  • median(): This function calculates the median along a specified axis.
  • mode(): This function calculates the mode() along a specified axis.
  • sum(): This function finds the sum of values along a specified axis.
  • var(): It calculates the variance along a specified axis.

27. How can one identify the items in series A that are absent from series B?

Ans: You can use the steps below to find the series A items that are missing from series B:

  • Make a collection of the series B items. The set() function can be used to accomplish this.
     
  • Add the series B set of items to the series A group of things. To accomplish this, use the - operator.
     
  • The items in series A that are missing from series B will make up the resulting set.

28. How can we convert DataFrame to an excel file?

Ans: We can use the to_excel() function to convert a DataFrame to an excel file using Pandas in Python. To use this function, you can simply provide the DataFrame and the desired file name as an argument.

Pandas Interview Questions and Answers for Experienced

This section will discuss some of the more challenging Pandas Interview Questions. While knowing easy and medium-level questions is necessary, the more complicated questions will set you above other candidates in the interview. Let us go through some of the more difficult Pandas Interview Questions.

29. What is data aggregation?

Ans: The main work of data aggregation is to apply some assembly to one or more columns. It uses sum to return the sum of the values, min to return the minimum, and max to return the maximum value for the requested axis.

30. What is Multiple Indexing?

Ans: A method for indexing a Pandas DataFrame with numerous layers is multiple indexing, commonly referred to as hierarchical indexing. As a result, you can design indexes with numerous dimensions, including those for data with time series, locations, or categories.

31. What is concat() in Pandas?

Ans: The .concat() method stacks multiple DataFrames vertically or connects them horizontally after aligning them on an index.

32. What is GroupBy() in Pandas?

Ans: The GroupBy() functions’ main task is to split the data into various groups. It allows rearranging the data by utilising them in real-world data sets.

33. How to sort the DataFrame?

Ans: To sort the DataFrame use the DataFrame.sort_values() function. It sorts the DataFrame row or column-wise. The important parameters of the sort function are:

  • axis: specifies whether to sort for rows (0) or columns (1)
  • by: specifies which column or rows determine sorting
  • ascending: specifies whether to sort the DataFrame in ascending or descending order

34. How can one create an empty DataFrame in Pandas?

Ans: You can use the pd.DataFrame() function in Pandas without giving any arguments to build a blank DataFrame. A DataFrame without any rows or columns will result from this.

Here is an example of how to make a blank Python DataFrame:

import pandas as pd
# Create an empty DataFrame
df = pd.DataFrame()
# Print the DataFrame
print(df)

35. How to split a DataFrame based on boolean criteria?

Ans: To split the DataFrame, first create a mask to separate the data frame and then use the (~) inverse operator to take the complement of the mask.

36. What do describe() percentiles values represent?

Ans: The percentiles describe the data distribution we are working on. The median is represented by 50, whereas the lower and upper borders are at 25 and 75, respectively. Using this, we can get a clearer idea of how skewed is our data.

37. How can you merge data on common columns or indices?

Ans: To merge, use the .merge() method which is similar to database-style joins. We have the inner, outer, left and right merge operations. An inner merge merges left and right data frames keeping only the common values. Left and right merge operations keep all the rows from their side and add empty / Nan values on the missing opposite side. An outer merge returns all the rows from the left and right sides.

38. How to write DataFrame to PostgreSQL table?

Ans: You will have to use the to_sql module, create an SQLAlchemy engine, and then write DataFrame to the SQL table.

39. How to convert continuous values to discrete values in Pandas?

Ans: You will have to use either cut() or qcut() functions:

  • cut() bins the data on values. We use it when we need evenly spaced values in bins. This function will use values rather than frequencies to sort the data.
  • qcut() bins the data based on sample quantities. We use it to study data by quantities. It will divide an equal number of data in each bin.

40. How are iloc() and loc() different?

Ans: The major difference between iloc() and loc() is that the iloc() function is used for selecting data based on integer-based indexing. While loc() is used to select data based on label-based indexing.

41. Explain the difference between join() and merge() in Pandas?

Ans: The major difference between join() and merge() in Pandas is below.

join(): It is a method for combining DataFrames based on their indexes. Left join is the default join and it is a convenient way to merge DataFrames.

merge(): This allows merging DataFrames based on specified column values. It supports inner, outer left, and right joins. It can merge DataFrames on one or more columns based on common values to combine the data.

42.  Explain the difference(s) between merge() and concat() in Pandas?

Ans: The major difference between merge() and concat() in Pandas is below.

merge(): It combines DataFrames based on common columns and performs various joins such as inner, outer, right, and left.

concat(): This function concatenates DataFrames along with a particular axis. It provide no relationship between the data in the DataFrames.

43.  Explain the difference between interpolate() and fillna() in Pandas?

Ans: The major difference between interpolate() and fillna() in Pandas is below.

interpolate(): It is the method that is used to fill missing values in DataFrame by estimating values based on existing data.

fillna(): Maily fillna() is used to replace missing data or values with the appropriate values.

44. What are the types of conversion methods in Pandas?

Ans: The conversion methods are:

  • to_numeric() - converts non numeric to numeric type
  • astype() - converts any type to any other type, it can also convert to    categorical types
  • convert_dtypes() - converts DataFrames to best dtype 
  • infer_objects() - a utility method to convert object columns holding Python objects to a pandas type if possible

Conclusion

In this article, we have discussed pandas interview questions in detail. We started with a basic introduction to the pandas and then discussed Pandas Interview Questions thoroughly.

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