Creating a dataframe from Pandas series

Last Updated : 29 Sep, 2023

Series is a type of list in Pandas that can take integer values, string values, double values, and more. But in Pandas Series we return an object in the form of a list, having an index starting from 0 to n, Where n is the length of values in the series. Later in this article, we will discuss Dataframes in pandas, but we first need to understand the main difference between Series and Dataframe.

Series can only contain a single list with an index, whereas Dataframe can be made of more than one series or we can say that a Dataframe is a collection of series that can be used to analyze the data.

Creating Pandas DataFrames from Series

Python3

# importing pandas library
import pandas as pd

# Creating a list
author = ['Jitender', 'Purnima', 
          'Arpit', 'Jyoti']
# Creating a Series by passing list 
# variable to Series() function
auth_series = pd.Series(author)
# Printing Series
print(auth_series)

Output:

0    Jitender
1     Purnima
2       Arpit
3       Jyoti
dtype: object

Let's check the type of Series:

Python3

print(type(auth_series))

Output:

<class 'pandas.core.series.Series'>

Create DataFrame From Multiple Series

We have created two lists 'author' and article' which have been passed to pd.Series() functions to create two Series. After creating the Series, we created a dictionary and passed Series objects as values of the dictionary, and the keys of the dictionary will be served as Columns of the Dataframe.

Python3

# Importing Pandas library
import pandas as pd

# Creating two lists
author = ['Jitender', 'Purnima',
          'Arpit', 'Jyoti']
article = [210, 211, 114, 178]

# Creating two Series by passing lists
auth_series = pd.Series(author)
article_series = pd.Series(article)

# Creating a dictionary by passing Series objects as values
frame = {'Author': auth_series,
         'Article': article_series}

# Creating DataFrame by passing Dictionary
result = pd.DataFrame(frame)

# Printing elements of Dataframe
print(result)

Output:

     Author  Article
0  Jitender      210
1   Purnima      211
2     Arpit      114
3     Jyoti      178

Add a Column in Pandas Dataframe

We have added one more series externally named as the age of the authors, then directly added this series in the Pandas Dataframe.

Python3

# Importing pandas library
import pandas as pd
# Creating Series
auth_series = pd.Series(['Jitender', 
          'Purnima', 'Arpit', 'Jyoti'])
article_series = pd.Series([210, 211, 114, 178])
# Creating Dictionary
frame = {'Author': auth_series, 
         'Article': article_series}
# Creating Dataframe
result = pd.DataFrame(frame)
# Creating another list
age = [21, 21, 24, 23]
# Creating new column in the dataframe by 
# providing s Series created using list
result['Age'] = pd.Series(age)
# Printing dataframe
print(result)

Output:

     Author  Article  Age
0  Jitender      210   21
1   Purnima      211   21
2     Arpit      114   24
3     Jyoti      178   23

Missing value in Pandas Dataframe

Remember one thing if any value is missing then by default it will be converted into NaN value, i.e, null by default.

Python3

# Importing pandas library
import pandas as pd
# Creating Series
auth_series = pd.Series(['Jitender',
              'Purnima', 'Arpit', 'Jyoti'])
article_series = pd.Series([210, 211, 114, 178])
# Creating Dictionary
frame = {'Author': auth_series,
         'Article': article_series}
# Creating Dataframe
result = pd.DataFrame(frame)
# Creating another list
age = [21, 21, 24]
# Creating new column in the dataframe by 
# providing s Series created using list
result['Age'] = pd.Series(age)
# Printing dataframe
print(result)

Output:

     Author  Article   Age
0  Jitender      210  21.0
1   Purnima      211  21.0
2     Arpit      114  23.0
3     Jyoti      178   NaN

Creating a Dataframe using a dictionary of Series

Here, we have passed a dictionary that has been created using a series as values then passed this dictionary to create a Dataframe. We can see while creating a Dataframe using Python Dictionary, the keys of the dictionary will become Columns and values will become Rows.

Python3

# Importing pandas library
import pandas as pd
# Creating dictionary of Series
dict1 = {'Auth_Name': pd.Series(['Jitender', 
                  'Purnima', 'Arpit', 'Jyoti']),
         'Author_Book_No':\
                 pd.Series([210, 211, 114, 178]),
         'Age': pd.Series([21, 21, 24, 23])}

# Creating Dataframe
df = pd.DataFrame(dict1)
# Printing dataframe
print(df)

Output:

   Auth_Name  Auth_Book_No  Age
0  Jitender           210   21
1   Purnima           211   21
2     Arpit           114   24
3     Jyoti           178   23

Explicit Indexing in Pandas Dataframe

Here we can see after providing an index to the dataframe explicitly, it has filled all data with NaN values since we have created this dataframe using Series and Series has its own default indices(0,1,2) which is why when indices of both dataframe and Series do not match, we got all NaN values.

Python3

# Importing pandas library
import pandas as pd
# Creating dictionary of Series
dict1 = {'Auth_Name': pd.Series(['Jitender',
                      'Purnima', 'Arpit', 'Jyoti']),
    'Author_Book_No': pd.Series([210, 211, 114, 178]),
     'Age': pd.Series([21, 21, 24, 23])}

# Creating Dataframe
df = pd.DataFrame(dict1, index=['SNo1', 'SNo2', 'SNo3', 'SNo4'])
# Printing dataframe
print(df)

Output:

          Auth_Name     Author_Book_No  Age
SNo1       NaN             NaN          NaN
SNo2       NaN             NaN          NaN
SNo3       NaN             NaN          NaN
SNo4       NaN             NaN          NaN

Here, we can rectify this problem by providing the same index values to every Series element.

Python3

# This code is provided by Sheetal Verma
# Importing pandas library
import pandas as pd
# Creating dictionary of Series
dict1 = {'Auth_Name': pd.Series(['Jitender',
              'Purnima', 'Arpit', 'Jyoti'],
          index=['SNo1', 'SNo2', 'SNo3', 'SNo4']),
         'Author_Book_No': pd.Series([210, 211, 114, 178],
                   index=['SNo1', 'SNo2', 'SNo3', 'SNo4']),
         'Age': pd.Series([21, 21, 24, 23],
                index=['SNo1', 'SNo2', 'SNo3', 'SNo4'])}

# Creating Dataframe
df = pd.DataFrame(dict1, index=['SNo1', 'SNo2', 'SNo3', 'SNo4'])
# Printing dataframe
print(df)

Output:

      Auth_Name       Author_Book_No  Age
SNo1  Jitender             210        21
SNo2   Purnima             211        21
SNo3     Arpit             114        24
SNo4     Jyoti             178        23

Construct a DataFrame in Pandas using string data

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Article Tags :

Practice Tags :

python

Creating a dataframe from Pandas series

Creating Pandas DataFrames from Series

Create DataFrame From Multiple Series

Add a Column in Pandas Dataframe

Missing value in Pandas Dataframe

Creating a Dataframe using a dictionary of Series

Explicit Indexing in Pandas Dataframe

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