Pandas - Manipulation data column-wise

import pandas as pd

iris = pd.read_csv("archive.ics.uci.edu/ml/machine-learning-dat..")

df = iris.copy()

df.columns = ['sl', 'sw', 'pl', 'pw', 'flower_type']

1) deleting a column

/ Method 1

df.drop('sl', axis = 1, inplace = True)

print(df)

149 rows × 4 columns

Here, axis denotes to check it in column wise i.e., axis = 1 -> column_number = 1.

inplace = True, if not used it won't perform any changes in df instead creates a copy of it and performs the action in the copy.

/ Method 2

del df['sw']

print(df)

149 rows × 3 columns

2) adding a column

df["diff_pl_pw"] = df["pl"] - df["pw"]

print(df)

149 rows × 4 columns

You can also do this but make sure the no. of entries is equal to the row count.

Eg:- df["sl"] = [1, 2, 3,......, 148] / the dots must be replaced with values.