In this article we will discuss different ways to create an empty DataFrame and then fill data in it later by either adding rows or columns.
Suppose we want to create an empty DataFrame first and then append data into it at later stages. Let’s see how to do that,
Import python’s pandas module like this,
import pandas as pd
Create an empty DataFrame with only column names but no rows
Suppose we know the column names of our DataFrame but we don’t have any data as of now. So we will create an empty DataFrame with only column names like this,
Frequently Asked:
# Creating an empty Dataframe with column names only
dfObj = pd.DataFrame(columns=['User_ID', 'UserName', 'Action'])
print("Empty Dataframe ", dfObj, sep='\n')
Contents of the created empty DataFrame will be,
Columns: [User_ID, UserName, Action] Index: []
How it worked ?
Dataframe class provides a constructor to create Dataframe object by passing column names , index names & data in argument like this,
def __init__(self, data=None, index=None, columns=None, dtype=None,
To create an empty dataframe object we passed columns argument only and for index & data default arguments will be used.
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Append rows to empty DataFrame
As we have created an empty DataFrame, so let’s see how to add rows to it,
# Append rows in Empty Dataframe by adding dictionaries
dfObj = dfObj.append({'User_ID': 23, 'UserName': 'Riti', 'Action': 'Login'}, ignore_index=True)
dfObj = dfObj.append({'User_ID': 24, 'UserName': 'Aadi', 'Action': 'Logout'}, ignore_index=True)
dfObj = dfObj.append({'User_ID': 25, 'UserName': 'Jack', 'Action': 'Login'}, ignore_index=True)
print("Dataframe Contens ", dfObj, sep='\n')
Output:
User_ID UserName Action 0 23 Riti Login 1 24 Aadi Logout 2 25 Jack Login
Three rows were added to the DataFrame.
Create an complete empty DataFrame without any column name or indices
We can create a complete empty dataframe by just calling the Dataframe class constructor without any arguments like this,
# Create an completely empty Dataframe without any column names, indices or data dfObj = pd.DataFrame()
As we have not passed any arguments, so default value of all arguments will be None and it will create an empty dataframe dfObj. It’s contents are as follows,
Columns: [] Index: []
Now let’s see how to append columns with data to this empty Dataframe,
Appends columns to an empty DataFrame
# Append columns to the Empty DataFrame
dfObj['UserName'] = ['Riti', 'Aadi', 'Jack']
dfObj['Name'] = ['Riti', 'Aadi', 'Jack']
dfObj['Name'] = ['Riti', 'Aadi', 'Jack']
print("Dataframe Contents ", dfObj, sep='\n')
Output:
Dataframe Contens UserName Name 0 Riti Riti 1 Aadi Aadi 2 Jack Jack
Create an empty Dataframe with column names & row indices but no data
It might be possible in some cases that we know the column names & row indices at start but we don’t have data yet. So we will create an empty DataFrame and add data to it at later stages like this,
# Create an empty Dataframe with columns or indices
dfObj = pd.DataFrame(columns=['User_ID', 'UserName', 'Action'], index=['a', 'b', 'c'])
print("Empty Dataframe", dfObj, sep='\n')
Here we passed the columns & index arguments to Dataframe constructor but without data argument. So, it will create an empty dataframe with all data as NaN.
User_ID UserName Action a NaN NaN NaN b NaN NaN NaN c NaN NaN NaN
Add rows to an empty dataframe at existing index
dfObj.loc['a'] = [23, 'Riti', 'Login']
dfObj.loc['b'] = [24, 'Aadi', 'Logout']
dfObj.loc['c'] = [25, 'Jack', 'Login']
print("Dataframe Contents ", dfObj, sep='\n')
output:
Dataframe Contents User_ID UserName Action a 23 Riti Login b 24 Aadi Logout c 25 Jack Login
Complete example is as follows:
import pandas as pd
def main():
print('*** Create an empty DataFrame with only column names ***')
# Creating an empty Dataframe with column names only
dfObj = pd.DataFrame(columns=['User_ID', 'UserName', 'Action'])
print("Empty Dataframe ", dfObj, sep='\n')
print('*** Appends rows to an empty DataFrame using dictionary with default index***')
# Append rows in Empty Dataframe by adding dictionaries
dfObj = dfObj.append({'User_ID': 23, 'UserName': 'Riti', 'Action': 'Login'}, ignore_index=True)
dfObj = dfObj.append({'User_ID': 24, 'UserName': 'Aadi', 'Action': 'Logout'}, ignore_index=True)
dfObj = dfObj.append({'User_ID': 25, 'UserName': 'Jack', 'Action': 'Login'}, ignore_index=True)
print("Dataframe Contens ", dfObj, sep='\n')
print('*** Create an completely empty DataFrame ***')
# Create an completely empty Dataframe without any column names, indices or data
dfObj = pd.DataFrame()
print("Empty Dataframe", dfObj, sep='\n')
print('*** Appends columns to an empty DataFrame ***')
# Append columns to the Empty DataFrame
dfObj['UserName'] = ['Riti', 'Aadi', 'Jack']
dfObj['Name'] = ['Riti', 'Aadi', 'Jack']
dfObj['Name'] = ['Riti', 'Aadi', 'Jack']
print("Dataframe Contents ", dfObj, sep='\n')
print('*** Create an empty DataFrame with column and index names but no Data ***')
# Create an empty Dataframe with columns or indices
dfObj = pd.DataFrame(columns=['User_ID', 'UserName', 'Action'], index=['a', 'b', 'c'])
print("Empty Dataframe", dfObj, sep='\n')
print('*** Appends rows to an empty DataFrame on an existing index***')
dfObj.loc['a'] = [23, 'Riti', 'Login']
dfObj.loc['b'] = [24, 'Aadi', 'Logout']
dfObj.loc['c'] = [25, 'Jack', 'Login']
print("Dataframe Contents ", dfObj, sep='\n')
if __name__ == '__main__':
main()
Output:
*** Create an empty DataFrame with only column names *** Empty Dataframe Empty DataFrame Columns: [User_ID, UserName, Action] Index: [] *** Appends rows to an empty DataFrame using dictionary with default index*** Dataframe Contens User_ID UserName Action 0 23 Riti Login 1 24 Aadi Logout 2 25 Jack Login *** Create an completely empty DataFrame *** Empty Dataframe Empty DataFrame Columns: [] Index: [] *** Appends columns to an empty DataFrame *** Dataframe Contents UserName Name 0 Riti Riti 1 Aadi Aadi 2 Jack Jack *** Create an empty DataFrame with column and index names but no Data *** Empty Dataframe User_ID UserName Action a NaN NaN NaN b NaN NaN NaN c NaN NaN NaN *** Appends rows to an empty DataFrame on an existing index*** Dataframe Contents User_ID UserName Action a 23 Riti Login b 24 Aadi Logout c 25 Jack Login
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