In this article, we will discuss how to add / append a single or multiple rows in a dataframe using dataframe.append() or loc & iloc.
Table of Contents
Overview of pandas dataframe append()
Pandas Dataframe provides a function dataframe.append() to add rows to a dataframe i.e.
DataFrame.append(other, ignore_index=False, verify_integrity=False, sort=None)
Here, the ‘other’ parameter can be a DataFrame or Series or Dictionary or list of these. Also, if ignore_index is True then it will not use indexes.
Examples of adding row to the dataframe
Suppose we have a dataframe df, whose contents are as follows,
Name Age City Country a jack 34 Sydeny Australia b Riti 30 Delhi India c Vikas 31 Mumbai India d Neelu 32 Bangalore India e John 16 New York US f Mike 17 las vegas US
Add dictionary as a row to dataframe
In dataframe.append() we can pass a dictionary of key value pairs i.e.
- key = Column name
- Value = Value at that column in new row
Let’s add a new row in above dataframe by passing dictionary i.e.
Frequently Asked:
# Pass the row elements as key value pairs to append() function
mod_df = df.append({'Name' : 'Sahil',
'Age' : 22} ,
ignore_index=True)
print('Modified Dataframe')
print(mod_df)
It will not modify the existing dataframe object mod_df, it will return a new dataframe containing copy of contents of existing dataframe and with a new row appended at it’s end. Contents of the dataframe returned are,
Modified Dataframe
Name Age City Country
0 jack 34 Sydeny Australia
1 Riti 30 Delhi India
2 Vikas 31 Mumbai India
3 Neelu 32 Bangalore India
4 John 16 New York US
5 Mike 17 las vegas US
6 Sahil 22 NaN NaN
New DataFrame’s index is not same as original dataframe because ignore_index is passed as True in append() function. Also, for columns which were not present in the dictionary NaN value is added.
Passing ignore_index=True is necessary while passing dictionary or series otherwise following TypeError error will come i.e.
“TypeError: Can only append a Series if ignore_index=True or if the Series has a name”
Complete example to add a dictionary as row to the dataframe is as follows,
import pandas as pd
# List of Tuples
students = [ ('jack', 34, 'Sydeny' , 'Australia') ,
('Riti', 30, 'Delhi' , 'India' ) ,
('Vikas', 31, 'Mumbai' , 'India' ) ,
('Neelu', 32, 'Bangalore' , 'India' ) ,
('John', 16, 'New York' , 'US') ,
('Mike', 17, 'las vegas' , 'US') ]
#Create a DataFrame object
df = pd.DataFrame( students,
columns = ['Name' , 'Age', 'City' , 'Country'],
index=['a', 'b', 'c' , 'd' , 'e' , 'f'])
print('Original Dataframe')
print(df)
# Pass the row elements as key value pairs to append() function
mod_df = df.append({'Name' : 'Sahil',
'Age' : 22} ,
ignore_index=True)
print('Modified Dataframe')
print(mod_df)
Output:
Original Dataframe
Name Age City Country
a jack 34 Sydeny Australia
b Riti 30 Delhi India
c Vikas 31 Mumbai India
d Neelu 32 Bangalore India
e John 16 New York US
f Mike 17 las vegas US
Modified Dataframe
Name Age City Country
0 jack 34 Sydeny Australia
1 Riti 30 Delhi India
2 Vikas 31 Mumbai India
3 Neelu 32 Bangalore India
4 John 16 New York US
5 Mike 17 las vegas US
6 Sahil 22 NaN NaN
Add Series as a row in the dataframe
We can also pass a series object to the append() function to append a new row to the dataframe i.e.
# A series object with same index as dataframe
series_obj = pd.Series( ['Raju', 21, 'Bangalore', 'India'],
index=dfObj.columns )
# Add a series as a row to the dataframe
mod_df = dfObj.append( series_obj,
ignore_index=True)
While creating a series object we passed the index names same as index of dataframe. Contents of the dataframe returned are,
Modified Dataframe
Name Age City Country
0 jack 34 Sydeny Australia
1 Riti 30 Delhi India
2 Vikas 31 Mumbai India
3 Neelu 32 Bangalore India
4 John 16 New York US
5 Mike 17 las vegas US
6 Raju 21 Bangalore India
Checkout the complete example to a append a series as row to dataframe,
import pandas as pd
# List of Tuples
students = [ ('jack', 34, 'Sydeny' , 'Australia') ,
('Riti', 30, 'Delhi' , 'India' ) ,
('Vikas', 31, 'Mumbai' , 'India' ) ,
('Neelu', 32, 'Bangalore' , 'India' ) ,
('John', 16, 'New York' , 'US') ,
('Mike', 17, 'las vegas' , 'US') ]
#Create a DataFrame object
df = pd.DataFrame( students,
columns = ['Name' , 'Age', 'City' , 'Country'],
index=['a', 'b', 'c' , 'd' , 'e' , 'f'])
print('Original Dataframe')
print(df)
# A series object with same index as dataframe
series_obj = pd.Series( ['Raju', 21, 'Bangalore', 'India'],
index=df.columns )
# Add a series as a row to the dataframe
mod_df = df.append( series_obj,
ignore_index=True)
print('Modified Dataframe')
print(mod_df)
Output:
Original Dataframe
Name Age City Country
a jack 34 Sydeny Australia
b Riti 30 Delhi India
c Vikas 31 Mumbai India
d Neelu 32 Bangalore India
e John 16 New York US
f Mike 17 las vegas US
Modified Dataframe
Name Age City Country
0 jack 34 Sydeny Australia
1 Riti 30 Delhi India
2 Vikas 31 Mumbai India
3 Neelu 32 Bangalore India
4 John 16 New York US
5 Mike 17 las vegas US
6 Raju 21 Bangalore India
Add multiple rows to pandas dataframe
We can pass a list of series too in the dataframe.append() for appending multiple rows in dataframe. For example, we can create a list of series with same column names as dataframe i.e.
# List of series with same Index as datframe
listOfSeries = [pd.Series(['Luke', 21, 'Bangalore', 'India'], index=df.columns ) ,
pd.Series(['Sam', 22, 'Tokyo', 'Japan'], index=df.columns ) ,
pd.Series(['Rocky', 23, 'Las Vegas', 'US'], index=df.columns ) ]
Now pass this list of series to the append() function i.e.
# Pass a list of series to the append() to add
# multiple rows to dataframe
mod_df = df.append( listOfSeries,
ignore_index=True)
Contents of the dataframe returned are,
Modified Dataframe
Name Age City Country
0 jack 34 Sydeny Australia
1 Riti 30 Delhi India
2 Vikas 31 Mumbai India
3 Neelu 32 Bangalore India
4 John 16 New York US
5 Mike 17 las vegas US
6 Luke 21 Bangalore India
7 Sam 22 Tokyo Japan
8 Rocky 23 Las Vegas US
Complete example to add multiple rows to dataframe is as follows,
import pandas as pd
# List of Tuples
students = [ ('jack', 34, 'Sydeny' , 'Australia') ,
('Riti', 30, 'Delhi' , 'India' ) ,
('Vikas', 31, 'Mumbai' , 'India' ) ,
('Neelu', 32, 'Bangalore' , 'India' ) ,
('John', 16, 'New York' , 'US') ,
('Mike', 17, 'las vegas' , 'US') ]
#Create a DataFrame object
df = pd.DataFrame( students,
columns = ['Name' , 'Age', 'City' , 'Country'],
index=['a', 'b', 'c' , 'd' , 'e' , 'f'])
print('Original Dataframe')
print(df)
# List of series with same Index as datframe
listOfSeries = [pd.Series(['Luke', 21, 'Bangalore', 'India'], index=df.columns ) ,
pd.Series(['Sam', 22, 'Tokyo', 'Japan'], index=df.columns ) ,
pd.Series(['Rocky', 23, 'Las Vegas', 'US'], index=df.columns ) ]
# Pass a list of series to the append() to add
# multiple rows to dataframe
mod_df = df.append( listOfSeries,
ignore_index=True)
print('Modified Dataframe')
print(mod_df)
Output
Original Dataframe
Name Age City Country
a jack 34 Sydeny Australia
b Riti 30 Delhi India
c Vikas 31 Mumbai India
d Neelu 32 Bangalore India
e John 16 New York US
f Mike 17 las vegas US
Modified Dataframe
Name Age City Country
0 jack 34 Sydeny Australia
1 Riti 30 Delhi India
2 Vikas 31 Mumbai India
3 Neelu 32 Bangalore India
4 John 16 New York US
5 Mike 17 las vegas US
6 Luke 21 Bangalore India
7 Sam 22 Tokyo Japan
8 Rocky 23 Las Vegas US
Add row from one dataframe to another dataframe
We can select a row from dataframe by its name using loc[] attribute and the pass the selected row as an argument to the append() function. It will add the that row to the another dataframe. Let’s see an example where we will select a row with index label ‘b’ and append it to another dataframe using append(). For example,
import pandas as pd
# List of Tuples
students = [ ('jack', 34, 'Sydeny' , 'Australia') ,
('Riti', 30, 'Delhi' , 'India' ) ,
('Vikas', 31, 'Mumbai' , 'India' ) ,
('Neelu', 32, 'Bangalore' , 'India' ) ,
('John', 16, 'New York' , 'US') ,
('Mike', 17, 'las vegas' , 'US') ]
#Create a DataFrame object
df = pd.DataFrame( students,
columns = ['Name' , 'Age', 'City' , 'Country'],
index=['a', 'b', 'c' , 'd' , 'e' , 'f'])
print('First Dataframe')
print(df)
# List of Tuples
students = [ ('Rahul', 22, 'Sydeny' , 'Australia') ,
('Parul', 23, 'Pune' , 'India') ]
#Create a DataFrame object
df_2 = pd.DataFrame(students,
columns = ['Name' , 'Age', 'City' , 'Country'],
index=['a', 'b'])
print('Second Dataframe')
print(df_2)
# add row at index b from dataframe df_2 to dataframe df
mod_df = df.append(df_2.loc['b'],
ignore_index=True)
print('Modified Dataframe')
print(mod_df)
Output
First Dataframe
Name Age City Country
a jack 34 Sydeny Australia
b Riti 30 Delhi India
c Vikas 31 Mumbai India
d Neelu 32 Bangalore India
e John 16 New York US
f Mike 17 las vegas US
Second Dataframe
Name Age City Country
a Rahul 22 Sydeny Australia
b Parul 23 Pune India
Modified Dataframe
Name Age City Country
0 jack 34 Sydeny Australia
1 Riti 30 Delhi India
2 Vikas 31 Mumbai India
3 Neelu 32 Bangalore India
4 John 16 New York US
5 Mike 17 las vegas US
6 Parul 23 Pune India
Add list as a row to pandas dataframe using loc[]
Adding a list as a row to the dataframe in pandas is very simple and easy. We can just pass the new index label in loc[] attribute and assign list object to it. For example,
# Add a new row at index k with values provided in list df.loc['k'] = ['Smriti', 26, 'Bangalore', 'India']
It will append a new row to the dataframe with index label ‘k’. Let’s see a complete example to append a list as row to the dataframe,
import pandas as pd
# List of Tuples
students = [ ('jack', 34, 'Sydeny' , 'Australia') ,
('Riti', 30, 'Delhi' , 'India' ) ,
('Vikas', 31, 'Mumbai' , 'India' ) ,
('Neelu', 32, 'Bangalore' , 'India' ) ,
('John', 16, 'New York' , 'US') ,
('Mike', 17, 'las vegas' , 'US') ]
#Create a DataFrame object
df = pd.DataFrame( students,
columns = ['Name' , 'Age', 'City' , 'Country'],
index=['a', 'b', 'c' , 'd' , 'e' , 'f'])
print('Original Dataframe')
print(df)
# Add a new row at index k with values provided in list
df.loc['k'] = ['Smriti', 26, 'Bangalore', 'India']
print('Modified Dataframe')
print(df)
Output:
Original Dataframe
Name Age City Country
a jack 34 Sydeny Australia
b Riti 30 Delhi India
c Vikas 31 Mumbai India
d Neelu 32 Bangalore India
e John 16 New York US
f Mike 17 las vegas US
Modified Dataframe
Name Age City Country
a jack 34 Sydeny Australia
b Riti 30 Delhi India
c Vikas 31 Mumbai India
d Neelu 32 Bangalore India
e John 16 New York US
f Mike 17 las vegas US
k Smriti 26 Bangalore India
Add a row in the dataframe at index position using iloc[]
We can add a row at specific position too in the dataframe using iloc[] attribute. Checkout the example, where we will add a list as the 3rd row the dataframe. For example,
import pandas as pd
# List of Tuples
students = [ ('jack', 34, 'Sydeny' , 'Australia') ,
('Riti', 30, 'Delhi' , 'India' ) ,
('Vikas', 31, 'Mumbai' , 'India' ) ,
('Neelu', 32, 'Bangalore' , 'India' ) ,
('John', 16, 'New York' , 'US') ,
('Mike', 17, 'las vegas' , 'US') ]
#Create a DataFrame object
df = pd.DataFrame( students,
columns = ['Name' , 'Age', 'City' , 'Country'],
index=['a', 'b', 'c' , 'd' , 'e' , 'f'])
print('Original Dataframe')
print(df)
# Add a new row at index position 2 with values provided in list
df.iloc[2] = ['Smriti', 26, 'Bangalore', 'India']
print('Modified Dataframe')
print(df)
Output:
Original Dataframe
Name Age City Country
a jack 34 Sydeny Australia
b Riti 30 Delhi India
c Vikas 31 Mumbai India
d Neelu 32 Bangalore India
e John 16 New York US
f Mike 17 las vegas US
Modified Dataframe
Name Age City Country
a jack 34 Sydeny Australia
b Riti 30 Delhi India
c Smriti 26 Bangalore India
d Neelu 32 Bangalore India
e John 16 New York US
f Mike 17 las vegas US
Summary:
We learned about different ways to add / append rows to the dataframe in pandas.
Thank you. It helped !
Thank you!
Thank you, you saved me hours of looking
thank you, my friend – this was such a helpful post!
it answered my exact question about adding using iloc and what order the columns would be, and it also showed me a few other things i didn’t know.