Add One Row to Pandas DataFrame
-
.loc[index]
Method to Add the Row to Pandas Dataframe With Lists - Append Dictionary as the Row to Add It to Pandas Dataframe
-
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
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