Table of Contents
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 byignoring index - Using
panads.concat()function withindex.
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
input_datais represents a list of datacolumnsrepresent the columns names for the dataindexrepresent 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,
- dataframe is the input dataframe
- 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,
- dataframe is the input dataframe
- 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,
- dataframe is the input dataframe
- row specifies a dictionary of values
- 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,
- dataframe is the input dataframe
- row specifies a dictionary of values
- 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
