This video shows how to concatenate data frames using the pandas library in Python. Data frame concatenation, also known as pasting or binding, just means joining together two data frames that have either the same columns or the same rows. In other words, using concatenate lets you add more rows or columns to an existing data frame. Concatenation should not be confused with join (merge) operations that involve combining the records of two data tables based on one or more shared key columns.

Code used in this Python Code Clip:

import pandas as pd

data = pd.DataFrame({“character”: [“Goku”,“Vegeta”, “Nappa”,“Gohan”,“Piccolo”],
“power level”: [12000, 16000, 4000, 1500, 3000]})

data

new_rows = pd.DataFrame({“character”: [“Tien”,“Yamcha”, “Krillin”],
“power level”: [2000, 1600, 2000]})

new_rows

Concatenate Data Frames by Rows

data2 = pd.concat([data, new_rows], # List of data Frames to concatenate
axis=0, # Axis = 0 to concat by row
ignore_index=True)

data2

new_cols = pd.DataFrame({“uniform color”: [“orange”, “blue”, “black”, “orange”,
“purple”, “green”, “orange”, “orange”],
“species”:[“saiyan”,“saiyan”,“saiyan”,“half saiyan”,
“namak”,“human”,“human”,“human”]})

new_cols

Concatenate Data Frames by Columns

pd.concat([data2, new_cols], # List of data Frames to concatenate
axis=1) # Axis = 1 to concat by column

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