Say you two dataframes of same size with same variables i.e. same columns and would like to concatenate them in to a single dataframe. Pandas’ concat() function can be handy in concatenating two or more such simple dataframes.
In this post, we will see examples of how to concatenate two dataframes into a single one. Let us load Pandas and NumPy.
# load pandas import pandas as pd # load numpy import numpy as np # check Pandas' version pd.__version__ '1.0.0'
We will first create two data frames from scratch using numpy.
df1 = pd.DataFrame(np.random.randint(20, size=(2,3)),
index=list('ij'),
columns=list('ABC'))
Since it is a toy example, the first data frame we created has just two rows.
df1 A B C i 1 0 11 j 11 16 9
Similarly, we create a second data frame from scratch with two rows.
df2 = pd.DataFrame(np.random.randint(20, size=(2,3)),
index=list('mn'),
columns=list('ABC'))
We can verify that both the dataframes have same columns.
df2
A B C
m 15 14 14
n 18 11 19
To concatenate the twp dataframes, we use concat() function with two dataframes as a list.
pd.concat([df1,df2])
A B C
i 1 0 11
j 11 16 9
m 15 14 14
n 18 11 19
We can see that the two dataframes are concatenated the way we wanted.
This post is part of the series on Pandas 101, a tutorial covering tips and tricks on using Pandas for data munging and analysis.
