top of page

Data Scientist Program


Free Online Data Science Training for Complete Beginners.

No prior coding knowledge required!

Concatenating objects in Pandas

The concat() function (in the main pandas namespace) does all of the heavy lifting of performing concatenation operations along an axis while performing optional set logic (union or intersection) of the indexes (if any) on the other axes. Note that I say “if any” because there is only a single possible axis of concatenation for Series.

Before diving into all of the details of concat and what it can do, here is a simple example:

import pandas as pd
df1 = pd.DataFrame(
"A": ["A0", "A1", "A2", "A3"],
"B": ["B0", "B1", "B2", "B3"],
"C": ["C0", "C1", "C2", "C3"],
"D": ["D0", "D1", "D2", "D3"],
index=[0, 1, 2, 3],

df2 = pd.DataFrame(
"A": ["A4", "A5", "A6", "A7"],
"B": ["B4", "B5", "B6", "B7"],
"C": ["C4", "C5", "C6", "C7"],
"D": ["D4", "D5", "D6", "D7"],
index=[4, 5, 6, 7],

df3 = pd.DataFrame(
"A": ["A8", "A9", "A10", "A11"],
"B": ["B8", "B9", "B10", "B11"],
"C": ["C8", "C9", "C10", "C11"],
"D": ["D8", "D9", "D10", "D11"],
index=[8, 9, 10, 11],

Lets put all the three into frames as

frames = [df1, df2, df3]

Now finally concatenating the data frames and storing into result as

result = pd.concat(frames)


Output will be the join of the all three dataframes as shown in the picture above.


Recent Posts

See All


bottom of page