How to Add One Row to Pandas DataFrame

  1. .Loc[index] Method to Add the Row to Pandas Dataframe With Lists
  2. Append Dictionary as the Row to Add It to Pandas Dataframe
  3. Dataframe append() Method to Add a Row

Pandas is designed to load a fully populated dataframe. We can add row one by one to pandas.Dataframe by using various approaches like .loc, dictionaries, pandas.concat() or DataFrame.append().

.Loc[index] Method to Add the Row to Pandas Dataframe With Lists

loc[index] takes the new list as a new row and add it to the given index of pandas.Dataframe.

Example Codes:

# python 3.x
import pandas as pd
# List of Tuples
fruit_list = [ ('Orange', 34, 'Yes' )]
#Create a DataFrame object
df = pd.DataFrame(fruit_list, columns = ['Name' , 'Price', 'Stock'])
#Add new ROW
df.loc[1]=[ 'Mango', 4, 'No' ]
df.loc[2]=[ 'Apple', 14, 'Yes' ]
print(df)

Output:

     Name  Price Stock
0  Orange     34   Yes
1   Mango      4    No
2   Apple     14   Yes
Warning

ignore_index shall be set to be True when we pass a dictionary to the append() function. Otherwise, it will raise errors.

Append Dictionary as the Row to Add It to Pandas Dataframe

The append() method can take the dictionary’s value directly as a row and add it to pandas dataframe.

Example Codes:

# python 3.x
import pandas as pd
# List of Tuples
fruit_list = [ ('Orange', 34, 'Yes' )]
#Create a DataFrame object
df = pd.DataFrame(fruit_list, columns = ['Name' , 'Price', 'Stock'])
#Add new ROW
df=df.append({'Name' : 'Apple' , 'Price' : 23, 'Stock' : 'No'} , ignore_index=True)
df=df.append({'Name' : 'Mango' , 'Price' : 13, 'Stock' : 'Yes'} , ignore_index=True)
print(df)

Output:

     Name  Price Stock
0  Orange     34   Yes
1   Apple     23    No
2   Mango     13   Yes

Dataframe append() Method to Add a Row

append() method could append rows of other dataframe to the end of the original dataframe, and return a new dataframe. Columns of the new dataframe which are not in the original datafarme are also added to the existing dataframe and the new cells’ values are filled with NaN.

Example Codes:

import pandas as pd
fruit_list = [ ('Orange', 34, 'Yes' )]

df = pd.DataFrame(fruit_list, columns = ['Name' , 'Price', 'Stock'])
print("Original DataFrame:")
print(df)
print('.............................')
print('.............................')

new_fruit_list = [ ('Apple', 34, 'Yes','small' )]

dfNew = pd.DataFrame(new_fruit_list, columns = ['Name' , 'Price', 'Stock','Type'])
print("Newly Created DataFrame:")
print(dfNew)
print('.............................')
print('.............................')

#append one dataframe to othher
df=df.append(dfNew,ignore_index=True)
print("Copying DataFrame to orignal...")
print(df)

ignore_index=True will ignore the index of the new dataframe and assign them a new index in the original dataframe.

Output:

Original DataFrame:
     Name  Price Stock
0  Orange     34   Yes
.............................
.............................
Newly Created DataFrame:
    Name  Price Stock   Type
0  Apple     34   Yes  small
.............................
.............................
Copying  DataFrame to original..:
     Name  Price Stock   Type
0  Orange     34   Yes    NaN
1   Apple     34   Yes  small

Related Article - Pandas DataFrame

  • How to Get a Value From a Cell of a Pandas DataFrame
  • How to Convert Index of a Pandas Dataframe Into a Column