Python Interview Questions & Answers 2026

Ans: A Series is a one-dimensional labeled array capable of holding any data type (integers, strings, floating point numbers, Python objects, etc.). It has to be remembered that unlike Python lists, a Series will always contain data of the same type. Let’s see how to create a Pandas Series from Dictionary. Using Series() method without index parameter.

Ans: Pandas Series is a one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.). The axis labels are collectively called index . Pandas Series is nothing but a column in an excel sheet. Creating a Pandas Series- In the real world, a Pandas Series will be created by loading the datasets from existing storage, storage can be SQL Database, CSV file, and Excel file. Pandas Series can be created from the lists, dictionary, and from a scalar value etc. Series can be created in different ways, here are some ways by which we create a series: Creating a series from array: In order to create a series from array, we have to import a numpy module and have to use array() function. # import pandas as pd import pandas as pd # import numpy as np import numpy as np # simple array data = np.array([‘g’,’e’,’e’,’k’,’s’]) ser = pd.Series(data) print(se

Ans: A Series is defined as a one-dimensional array that is capable of storing various data types. We can create a Pandas Series from Dictionary: Create a Series from dict: We can also create a Series from dict. If the dictionary object is being passed as an input and the index is not specified, then the dictionary keys are taken in a sorted order to construct the index. If index is passed, then values correspond to a particular label in the index will be extracted from the dictionary. importpandas as pd importnumpy as np info = {‘x’: 0., ‘y’ : 1., ‘z’ : 2.} a = pd.Series(info) print (a) Output: x 0.0 y 1.0 z 2.0 dtype: float64

Ans: We can create the copy of series by using the following syntax: pandas.Series.copy Series.copy(deep=True) The above statements make a deep copy that includes a copy of the data and the indices. If we set the value of deep to False, it will neither copy the indices nor the data. 25. ) How will you create an empty DataFrame in Pandas? Ans: A DataFrame is a widely used data structure of pandas and works with a two-dimensional array with labeled axes (rows and columns) It is defined as a standard way to store data and has two different indexes, i.e., row index and column index. Create an empty DataFrame: The below code shows how to create an empty DataFrame in Pandas: # importing the pandas library importpandas as pd info = pd.DataFrame() print (info) Output: Empty DataFrame Columns: [ ] Index: [ ]

Ans: We can remove items present in p2 from p1 using isin() method. import pandas as pd p1 = pd.Series([2, 4, 6, 8, 10]) p2 = pd.Series([8, 10, 12, 14, 16]) p1[~p1.isin(p2)] Solution 0 2 1 4 2 6 dtype: int64

Ans: We get all the items of p1 and p2 not common to both using below example: import pandas as pd import numpy as np p1 = pd.Series([2, 4, 6, 8, 10]) p2 = pd.Series([8, 10, 12, 14, 16]) p1[~p1.isin(p2)] p_u = pd.Series(np.union1d(p1, p2)) # union p_i = pd.Series(np.intersect1d(p1, p2)) # intersect p_u[~p_u.isin(p_i)] Output: 0 2 1 4 2 6 5 12 6 14 7 16 dtype: int64

Ans: We can compute the minimum, 25th percentile, median, 75th, and maximum of p as below example: import pandas as pd import numpy as np p = pd.Series(np.random.normal(14, 6, 22)) state = np.random.RandomState(120) p = pd.Series(state.normal(14, 6, 22)) percentile(p, q=[0, 25, 50, 75, 100]) Output: array([ 4.61498692, 12.15572753, 14.67780756, 17.58054104, 33.24975515])

Ans: We can calculate the frequency counts of each unique value p as below example: import pandas as pd import numpy as np p= pd.Series(np.take(list(‘pqrstu’), np.random.randint(6, size=17))) p = pd.Series(np.take(list(‘pqrstu’), np.random.randint(6, size=17))) value_counts() Output: s 4 r 4 q 3 p 3 u 3

Ans: Adding an Index to a DataFrame Pandas allow adding the inputs to the index argument if you create a DataFrame. It will make sure that you have the desired index. If you don?t specify inputs, the DataFrame contains, by default, a numerically valued index that starts with 0 and ends on the last row of the DataFrame. Adding Rows to a DataFrame We can use .loc, iloc, and ix to insert the rows in the DataFrame. The loc basically works for the labels of our index. It can be understood as if we insert in loc[4], which means we are looking for that values of DataFrame that have an index labeled 4. The iloc basically works for the positions in the index. It can be understood as if we insert in iloc[4], which means we are looking for the values of DataFrame that are present at index ‘4`. The ix is a complex case because if the index is integer-based, we pass a label to ix. The ix[4] means tha

import pandas as pd employees = pd.DataFrame( data = { 'Name' : [ 'John Doe' , 'William Spark' ], 'Occupation' : [ 'Chemist' , 'Statistician' ], 'Date Of Join' : [ '2018-01-25' , '2018-01-26' ], 'Age' : [ 23 , 24 ]}, index = [ 'Emp001' , 'Emp002' ], columns = [ 'Name' , 'Occupation' , 'Date Of Join' , 'Age' ]) print ( "n------------ BEFORE ----------------n" ) print (employees) employees.loc[ 'Emp003' ] = [ 'Sunny' , 'Programmer' , '2018-01-25' , 45 ] print ( "n------------ AFTER ----------------n" ) print (employees) OUTPUT : C:pandas>python example22.py ------------ BEFORE ---------------- Name Occupation Date Of Join Age Emp001 John Doe Chemist 2018-01-25 23 Emp002 William Spark Statistician 2018-01-26 24 ------------ AFTER ---------------- Name Occupation Date Of Join Age Emp001 John Doe Chemist 2018-01-25 23 Emp002 William Spark Statistician 2018-01-26 24 Emp003 Sunny Programmer 201

Ans: Deleting an Index from Your DataFrame If you want to remove the index from the DataFrame, you should have to do the following: Reset the index of DataFrame. Executing del df.index.name to remove the index name. Remove duplicate index values by resetting the index and drop the duplicate values from the index column. Remove an index with a row. Deleting a Column from Your DataFrame You can use the drop() method for deleting a column from the DataFrame. The axis argument that is passed to the drop() method is either 0 if it indicates the rows and 1 if it drops the columns. You can pass the argument inplace and set it to True to delete the column without reassign the DataFrame. You can also delete the duplicate values from the column by using the drop_duplicates() method. Removing a Row from Your DataFrame By using df.drop_duplicates(), we can remove duplicate rows from the DataFrame. Y

Ans: You can use the .rename method to give different values to the columns or the index values of DataFrame. There are the following ways to change index / columns names (labels) of pandas.DataFrame . Use pandas.DataFrame.rename() Change any index / columns names individually with dict Change all index / columns names with a function Use pandas.DataFrame.add_prefix() , pandas.DataFrame.add_suffix() Add prefix and suffix to columns name Update the index / columns attributes of pandas.DataFrame Replace all index / columns names set_index() method that sets an existing column as an index is also provided. See the following post for detail. Specify the original name and the new name in dict like {original name: new name} to index / columns of rename() . index is for index name and columns is for the columns name. If you want to change either, you need only specify one of index or columns .

Ans: Indexing in Pandas : Indexing in pandas means simply selecting particular rows and columns of data from a DataFrame. Indexing could mean selecting all the rows and some of the columns, some of the rows and all of the columns, or some of each of the rows and columns. Indexing can also be known as Subset Selection. Pandas Indexing using [ ] , .loc[] , .iloc[ ] , .ix[ ] There are a lot of ways to pull the elements, rows, and columns from a DataFrame. There are some indexing method in Pandas which help in getting an element from a DataFrame. These indexing methods appear very similar but behave very differently. Pandas support four types of Multi-axes indexing they are: Dataframe.[ ] ; This function also known as indexing operator Dataframe.loc[ ] : This function is used for labels. Dataframe.iloc[ ] : This function is used for positions or integer based Dataframe.ix[] : This function i

In order to select a single row, we put a single row label in a .ix function. This function act similar as .loc[ ] if we pass a row label as a argument of a function. # importing pandas package import pandas as pd # making data frame from csv file data = pd.read_csv( "nba.csv" , index_col = "Name" ) # retrieving row by ix method first = data.ix[ "Avery Bradley" ] print (first) Output :

Ans: Reindexing changes the row labels and column labels of a DataFrame. To reindex means to conform the data to match a given set of labels along a particular axis. Multiple operations can be accomplished through indexing like − Reorder the existing data to match a new set of labels. Insert missing value (NA) markers in label locations where no data for the label existed. Example import pandas as pd import numpy as np N=20 df = pd.DataFrame({ 'A': pd.date_range(start='2016-01-01',periods=N,freq='D'), 'x': np.linspace(0,stop=N-1,num=N), 'y': np.random.rand(N), 'C': np.random.choice(['Low','Medium','High'],N).tolist(), 'D': np.random.normal(100, 10, size=(N)).tolist() }) #reindex the DataFrame df_reindexed = df.reindex(index=[0,2,5], columns=['A', 'C', 'B']) print df_reindexed Its output is as follows − A C B 0 2016-01-01 Low NaN 2 2016-01-03 High NaN 5 2016-01-06 Low NaN 42. Define Multi

Ans: Import modules import pandas as pd Create a dataframe data = {'name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 'year': [2012, 2012, 2013, 2014, 2014], 'reports': [4, 24, 31, 2, 3]} df = pd.DataFrame(data, index = ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma']) df name reports year Cochice Jason 4 2012 Pima Molly 24 2012 Santa Cruz Tina 31 2013 Maricopa Jake 2 2014 Yuma Amy 3 2014 Delete a row df.drop(['Cochice', 'Pima']) Output : name reports year Santa Cruz Tina 31 2013 Maricopa Jake 2 2014 Yuma Amy 3 2014

Ans: Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Dataframe.add() method is used for addition of dataframe and other, element-wise (binary operator add). Equivalent to dataframe + other, but with support to substitute a fill_value for missing data in one of the inputs. Syntax: DataFrame.add(other, axis=’columns’, level=None, fill_value=None) Parameters: other :Series, DataFrame, or constant axis :{0, 1, ‘index’, ‘columns’} For Series input, axis to match Series index on fill_value : [None or float value, default None] Fill missing (NaN) values with this value. If both DataFrame locations are missing, the result will be missing. level : [int or name] Broadcast across a level, matching Index values on the passed MultiIndex

Ans: The below code demonstrates how to convert the string to date: From datetime import datetime # Define dates as the strings dmy_str1 = ‘Wednesday, July 14, 2018’ dmy_str2 = ’14/7/17′ dmy_str3 = ’14-07-2017′ # Define dates as the datetime objects dmy_dt1 = datetime.strptime(date_str1, ‘%A, %B %d, %Y’) dmy_dt2 = datetime.strptime(date_str2, ‘%m/%d/%y’) dmy_dt3 = datetime.strptime(date_str3, ‘%m-%d-%Y’) #Print the converted dates print(dmy_dt1) print(dmy_dt2) print(dmy_dt3) Output: 2017-07-14 00:00:00 2017-07-14 00:00:00 2018-07-14 00:00:00

Ans: The main task of Data Aggregation is to apply some aggregation 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 a maximum values for the requested axis. Examples import pandas as pd import numpy as np df = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9], [np.nan, np.nan, np.nan]], columns=['A', 'B', 'C']) print(df) # Aggregate these functions over the rows. print(df.agg(['sum', 'min'])) # Different aggregations per column. print(df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})) # Aggregate over the columns. print(df.agg("mean", axis="columns")) # Aggregate over the rows. print(df.agg("mean", axis="rows")) OUTPUT : A B C 0 1.0 2 3.0 1 4.0 5 6.0 2 7.0 8 9.0 3 NaN NaN NaN A B C sum 12.0 15.0 18.0 min 1.0 2.0 3.0 A B

Ans: In Pandas, there are different useful data operations for DataFrame, which are as follows: Row and column selection We can select any row and column of the DataFrame by passing the name of the rows and columns. When you select it from the DataFrame, it becomes one-dimensional and considered as Series. Filter Data We can filter the data by providing some of the boolean expressions in DataFrame. Null values A Null value occurs when no data is provided to the items. The various columns may contain no values, which are usually represented as NaN. 46. Define GroupBy in Pandas? Ans: Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas dataframe.groupby() function is used to split the data into groups based on some criteria.

Please refer to training materials for the detailed answer.

Ans: One can use apply() function in order to apply function to every row in given dataframe. Let’s see the ways we can do this task. Example # Import pandas package import pandas as pd # Function to add def add(a, b, c): return a + b + c def main(): # create a dictionary with # three fields each data = { 'A' :[ 1 , 2 , 3 ], 'B' :[ 4 , 5 , 6 ], 'C' :[ 7 , 8 , 9 ] } # Convert the dictionary into DataFrame df = pd.DataFrame(data) print ( "Original DataFrame:n" , df) df[ 'add' ] = df. apply ( lambda row : add(row[ 'A' ], row[ 'B' ], row[ 'C' ]), axis = 1 ) print ( 'nAfter Applying Function: ' ) # printing the new dataframe print (df) if __name__ = = '__main__' : main() Output: 60. How will you get the top 2 rows from a DataFrame in pandas? # Select the first 2 rows of the Dataframe dfObj1 = empDfObj.head(2) print(“First 2 rows of the Dataframe : “) print(dfObj1) Output: First 2 rows of the

Ans: To create a completely empty Pandas dataframe, we use do the following: import pandas as pd MyEmptydf = pd.DataFrame() This will create an empty dataframe with no columns or rows. To create an empty dataframe with three empty column (columns X, Y and Z), we do: df = pd.DataFrame(columns=[‘X’, ‘Y’, ‘Z’])

Ans: We can create a DataFrame using following ways: Lists Dict of ndarrays Example-1: Create a DataFrame using List: importpandas as pd # a list of strings a = [‘Python’, ‘Pandas’] # Calling DataFrame constructor on list info = pd.DataFrame(a) print(info) Output: 0 0 Python 1 Pandas Example-2: Create a DataFrame from dict of ndarrays: importpandas as pd info = {‘ID’:[101, 102, 103],’Department’ :[‘B.Sc’,’B.Tech’,’M.Tech’,]} info = pd.DataFrame(info) print (info) Output: ID Department 0 101 B.Sc 1 102 B.Tech 2 103 M.Tech

Ans. We can reshape the series p into a dataframe with 6 rows and 2 columns as below example: import pandas as pd import numpy as np p = pd.Series(np.random.randint(1, 7, 35)) # Input p = pd.Series(np.random.randint(1, 7, 35)) info = pd.DataFrame(p.values.reshape(7,5)) print(info) Output: 0 1 2 3 4 0 3 2 5 5 1 1 3 2 5 5 5 2 1 3 1 2 6 3 1 1 1 2 2 4 3 5 3 3 3 5 2 5 3 6 4 6 3 6 6 6 5

Ans: The Pandas Series.to_frame() function is used to convert the series object to the DataFrame. to_frame(name=None) name: Refers to the object. Its Default value is None. If it has one value, the passed name will be substituted for the series name. s = pd.Series([“a”, “b”, “c”], name=”vals”) to_frame() Output: vals 0 a 1 b 2 c

Ans: Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Let’s discuss all different ways of selecting multiple columns in a pandas DataFrame. Method #1: Basic Method Given a dictionary which contains Employee entity as keys and list of those entity as values. # Import pandas package import pandas as pd # Define a dictionary containing employee data data = { 'Name' :[ 'Jai' , 'Princi' , 'Gaurav' , 'Anuj' ], 'Age' :[ 27 , 24 , 22 , 32 ], 'Address' :[ 'Delhi' , 'Kanpur' , 'Allahabad' , 'Kannauj' ], 'Qualification' :[ 'Msc' , 'MA' , 'MCA' , 'Phd' ]} # Convert the dictionary into DataFrame df = pd.DataFrame(data) # select two columns df[[ 'Name' , 'Qualification' ]] Output: Select Second to fourth column. # Import pandas package imp

Ans: Pandas provide a unique method to retrieve rows from a Data frame. DataFrame.loc[] method is a method that takes only index labels and returns row or dataframe if the index label exists in the caller data frame. Syntax: pandas.DataFrame.loc[ ] Parameters: Index label: String or list of string of index label of rows Return type: Data frame or Series depending on parameters Example #1 : Extracting single Row In this example, Name column is made as the index column and then two single rows are extracted one by one in the form of series using index label of rows. # importing pandas package import pandas as pd # making data frame from csv file data = pd.read_csv( "nba.csv" , index_col = "Name" ) # retrieving row by loc method first = data.loc[ "Avery Bradley" ] second = data.loc[ "R.J. Hunter" ] print (first, "nnn" , second) Output: As shown in the output image, two series were returned

Ans: For performing some high-level mathematical functions, we can convert Pandas DataFrame to numpy arrays. It uses the DataFrame.to_numpy() function. The DataFrame.to_numpy() function is applied on the DataFrame that returns the numpy ndarray. Syntax: DataFrame.to_numpy(dtype=None, copy=False) Parameters dtype: It is an optional parameter that pass the dtype to numpy.asarray(). copy: It returns the boolean value that has the default value False. It ensures that the returned value is not a view on another array. Returns It returns the numpy.ndarray as an output. Example1: import pandas as pd pd.DataFrame({“P”: [2, 3], “Q”: [4, 5]}).to_numpy() info = pd.DataFrame({“P”: [2, 3], “Q”: [4.0, 5.8]}) info.to_numpy() info[‘R’] = pd.date_range(‘2000’, periods=2) info.to_numpy() Output : array([[2, 4.0, Timestamp('2000-01-01 00:00:00')], [3, 5.8, Timestamp('2000-01-02 00:00:00')]], dtype=object)

Ans : Pandas DataFrame is a two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). Arithmetic operations align on both row and column labels. It can be thought of as a dict-like container for Series objects. This is the primary data structure of the Pandas. Pandas DataFrame.empty attribute checks if the dataframe is empty or not. It return True if the dataframe is empty else it return False . Syntax: DataFrame.empty Parameter : None Returns : bool Example #1: Use DataFrame.empty attribute to check if the given dataframe is empty or not # importing pandas as pd import pandas as pd # Creating the DataFrame df = pd.DataFrame({ 'Weight' :[ 45 , 88 , 56 , 15 , 71 ], 'Name' :[ 'Sam' , 'Andrea' , 'Alex' , 'Robin' , 'Kia' ], 'Age' :[ 14 , 25 , 55 , 8 , 21 ]}) # Create the index index_ = [ 'Row_1' , 'Row_2' , 'Row_3' , 'Row_4' , 'Row_5

Ans: pandas_ml is a package which integrates pandas, scikit-learn, xgboost into one package for easy handling of data and creation of machine learning models Installation $ pip install pandas_ml Example >>> import pandas_ml as pdml >>> import sklearn.datasets as datasets # create ModelFrame instance from sklearn.datasets >>> df = pdml.ModelFrame(datasets.load_digits()) >>> type(df) # binarize data (features), not touching target >>> df.data = df.data.preprocessing.binarize() >>> df.head() .target 0 1 2 3 4 5 6 7 8 ... 54 55 56 57 58 59 60 61 62 63 0 0 0 0 1 1 1 1 0 0 0 ... 0 0 0 0 1 1 1 0 0 0 1 1 0 0 0 1 1 1 0 0 0 ... 0 0 0 0 0 1 1 1 0 0 2 2 0 0 0 1 1 1 0 0 0 ... 1 0 0 0 0 1 1 1 1 0 3 3 0 0 1 1 1 1 0 0 0 ... 1 0 0 0 1 1 1 1 0 0 4 4 0 0 0 1 1 0 0 0 0 ... 0 0 0 0 0 1 1 1 0 0 [5 rows x 65 columns] # split to training and test data >>> train_df, test_df = df.model_selection.train_test_split(

Ans: pandas-charm is a small Python package for getting character matrices (alignments) into and out of pandas. Use this library to make pandas interoperable with BioPython and DendroPy . Convert between the following objects: BioPython Multiple Seq Alignment pandas DataFrame DendroPy Character Matrix pandas DataFrame “Sequence dictionary” pandas DataFrame The code has been tested with Python 2.7, 3.5 and 3.6.

$ pip install pandas-charm You may consider installing pandas-charm and its required Python packages within a virtual environment in order to avoid cluttering your system’s Python path. See for example the environment management system conda or the package virtualenv .

Testing is carried out with pytest: $ pytest -v test_pandascharm.py Test coverage can be calculated with Coverage.py using the following commands: $ coverage run -m pytest $ coverage report -m pandascharm.py The code follow style conventions in PEP8, which can be checked with pycodestyle: $ pycodestyle pandascharm.py test_pandascharm.py setup.py

>>> import pandas as pd >>> import pandascharm as pc >>> import dendropy >>> dna_string = '3 5nt1 TCCAAnt2 TGCAAnt3 TG-AAn' >>> print(dna_string) 3 5 t1 TCCAA t2 TGCAA t3 TG-AA >>> matrix = dendropy.DnaCharacterMatrix.get( ... data=dna_string, schema='phylip') >>> df = pc.from_charmatrix(matrix) >>> df t1 t2 t3 0 T T T 1 C G G 2 C C - 3 A A A 4 A A A By default, characters are stored as rows and sequences as columns in the DataFrame. If you want rows to hold sequences, just transpose the matrix in pandas: >>> df.transpose() 0 1 2 3 4 t1 T C C A A t2 T G C A A t3 T G - A A