4 ways to add row to existing DataFrame in Pandas

Different methods to add row to existing DataFrame in pandas

In this tutorial we will discuss how to add row to existing pandas DataFrame using the following methods:

• Using loc[] function with index
• Using append() function
• Using panads.concat() function by ignoring index
• Using panads.concat() function with index.

 

Create pandas DataFrame with example data

DataFrame is a data structure used to store the data in two dimensional format. It is similar to table that stores the data in rows and columns. Rows represents the records/ tuples and columns refers to the attributes.

We can create the DataFrame by using pandas.DataFrame() method.

Syntax:

pandas.DataFrame(input_data,columns,index)

Parameters:

It will take mainly three parameters

1. input_data is represents a list of data
2. columns represent the columns names for the data
3. index represent the row numbers/values

We can also create a DataFrame using dictionary by skipping columns and indices.

Example: Python Program to create a dataframe for market data from a dictionary of food items by specifying the column names.

# import the module
import pandas

# consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

# pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

# display the dataframe
print(dataframe)

Output:

            id            name    cost  quantity
item-1  foo-23  ground-nut oil  567.00         1
item-2  foo-13         almonds  562.56         2
item-3  foo-02           flour   67.00         3
item-4  foo-31         cereals   76.09         2

 

Method 1 : Using loc[] function with index

Here , we are going to add a row by using loc[] function through index function. We need to specify the index position by len() function to insert a row at last position of the dataframe and assign the values of a row using  list.

Syntax:

dataframe.loc[len(dataframe.index)] =[list of values]

where,

1. dataframe is the input dataframe
2. list of values represents the row

Example:

Python program to add a row at last with the list of values - ['foo-45', 'wheat', 93,1]

# import the module
import pandas

# consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

# pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

# add row 
dataframe.loc[len(dataframe.index)] = ['foo-45', 'wheat', 93,1] 

# display the final dataframe
print(dataframe)

Output:

            id            name    cost  quantity
item-1  foo-23  ground-nut oil  567.00         1
item-2  foo-13         almonds  562.56         2
item-3  foo-02           flour   67.00         3
item-4  foo-31         cereals   76.09         2
4       foo-45           wheat   93.00         1

 

Method 2 : Using append() function

Here we are using append() function to add a row to an existing dataframe by ignoring the index. We are going to append a row which is created from a dictionary and exclude the index using ignore_index parameter. It is set to True.

Syntax:

dataframe.append(row, ignore_index = True)

where,

1. dataframe is the input dataframe
2. row specifies a dictionary of values

Example:

Python program to add a row by ignoring the index using append() function.

# import the module
import pandas

# consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

# pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

# define a  row 
final_row= {'id':'foo-45','name': 'wheat', 'cost':93,'quantity':1}

#append final_row
dataframe = dataframe.append(final_row, ignore_index = True)
            
# display the final dataframe
print(dataframe)

Output:

       id            name    cost  quantity
0  foo-23  ground-nut oil  567.00         1
1  foo-13         almonds  562.56         2
2  foo-02           flour   67.00         3
3  foo-31         cereals   76.09         2
4  foo-45           wheat   93.00         1

If we want to display the index, then we no need to specify the ignore_index parameter.

 

Example: Python program to display index by adding row

# import the module
import pandas

# consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

# pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

# define a  row 
final_row= {'id':'foo-45','name': 'wheat', 'cost':93,'quantity':1}

#append final_row
dataframe = pandas.concat([dataframe,pandas.DataFrame((final_row),index=['item-5'])])
            
# display the final dataframe
print(dataframe)

Output:

            id            name    cost  quantity
item-1  foo-23  ground-nut oil  567.00         1
item-2  foo-13         almonds  562.56         2
item-3  foo-02           flour   67.00         3
item-4  foo-31         cereals   76.09         2
item-5  foo-45           wheat   93.00         1

 

Method 3 : Using panads.concat() function by ignoring index

Here we are using pandas.concat() function to add a row to an existing dataframe by ignoring the index. We are going to append a row which is created from a dictionary and exclude the index using ignore_index parameter. It is set to True.

Syntax:

pandas.concat([dataframe,pandas.DataFrame((row),index)], ignore_index = True)

where,

1. dataframe is the input dataframe
2. row specifies a dictionary of values
3. index specifies row index

 

Example: Python program to add a row by ignoring  the index using append() function.

# import the module
import pandas

# consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

# pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

# define a  row 
final_row= {'id':'foo-45','name': 'wheat', 'cost':93,'quantity':1}

#append final_row
dataframe = pandas.concat([dataframe,pandas.DataFrame((final_row),index=['item-5'])], ignore_index = True)
            
# display the final dataframe
print(dataframe)

Output:

       id            name    cost  quantity
0  foo-23  ground-nut oil  567.00         1
1  foo-13         almonds  562.56         2
2  foo-02           flour   67.00         3
3  foo-31         cereals   76.09         2
4  foo-45           wheat   93.00         1

We can also add multiple rows at a time.

 

Example: In this example we are going to add 2 rows

# import the module
import pandas

# consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

# pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

# define a  row 
final_row= {'id':['foo-45','foo-25'],'name': ['wheat','ragi-powder'], 'cost':[89,93],'quantity':[3,1]}

#append final_row
dataframe = pandas.concat([dataframe,pandas.DataFrame((final_row),index=['item-5','item-6'])], ignore_index = True)
            
# display the final dataframe
print(dataframe)

Output:

       id            name    cost  quantity
0  foo-23  ground-nut oil  567.00         1
1  foo-13         almonds  562.56         2
2  foo-02           flour   67.00         3
3  foo-31         cereals   76.09         2
4  foo-45           wheat   89.00         3
5  foo-25     ragi-powder   93.00         1

 

Method 4 : Using panads.concat() function with index

Here we are using pandas.concat() function to add a row to an existing dataframe by including the index. We are going to append a row which is created from a dictionary and include the index using ignore_index parameter. It is set to False.

Syntax:

pandas.concat([dataframe,pandas.DataFrame((row),index)], ignore_index = False)

where,

1. dataframe is the input dataframe
2. row specifies a dictionary of values
3. index specifies row index

 

Example: Python program to add a row by including the index using append() function.

# import the module
import pandas

# consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

# pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

# define a  row 
final_row= {'id':'foo-45','name': 'wheat', 'cost':93,'quantity':1}

#append final_row
dataframe = pandas.concat([dataframe,pandas.DataFrame((final_row),index=['item-5'])], ignore_index = False)
            
# display the final dataframe
print(dataframe)

Output:

            id            name    cost  quantity
item-1  foo-23  ground-nut oil  567.00         1
item-2  foo-13         almonds  562.56         2
item-3  foo-02           flour   67.00         3
item-4  foo-31         cereals   76.09         2
item-5  foo-45           wheat   93.00         1

We can also add multiple rows at a time.

 

Example: In this example we are going to add 2 rows

# import the module
import pandas

# consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

# pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

# define a  row 
final_row= {'id':['foo-45','foo-25'],'name': ['wheat','ragi-powder'], 'cost':[89,93],'quantity':[3,1]}

#append final_row
dataframe = pandas.concat([dataframe,pandas.DataFrame((final_row),index=['item-5','item-6'])], ignore_index = False)
            
# display the final dataframe
print(dataframe)

Output:

            id            name    cost  quantity
item-1  foo-23  ground-nut oil  567.00         1
item-2  foo-13         almonds  562.56         2
item-3  foo-02           flour   67.00         3
item-4  foo-31         cereals   76.09         2
item-5  foo-45           wheat   89.00         3
item-6  foo-25     ragi-powder   93.00         1

 

Summary

In this tutorial we discussed how to add row/s to an existing dataframe using append(), loc[] and concat() functions. We also included and excluded indices with concat() function. We understand that we can only add multiple rows for an existing dataframe using concat() function , but not with append() function.

 

References

1. Pandas - append()
2. Pandas - concat()