Pandas — Missing Data

Let’s Continue the Python Exercises — Filling & Dropping Missing Data — #PySeries#Episode 11

Open your Colab notebook and here are the follow-up exercises!

print(“Hello Pandas — Missing Data!”)

Preparing DataFrame:

import numpy as np
import pandas as pd
d = {'A':[1,2, np.nan], 'B':[5, np.nan, np.nan], 'C':[1,2,3]}
df = pd.DataFrame(d)

Row ‘0’ has no missing Values, and Column ‘C’ has no missing values

df
## Dropping Rows w/ Missing Values (dropna)
df.dropna()

Dropping Columns w/ Missing Values

df.dropna(axis=1)

Specifying a Threshold

# If We set the threshold to be equal to 2 and run 
# this will went ahead and kept row 1 and 2, 
# because it has a maximum of 2 Nan values
df.dropna(thresh=2)

Filling in Missing Values (fillna)

# Again, here is my DF:
df

df.fillna(value = “Fill Value”)

Or to the mean of the column

df[‘A’].fillna(value=df[‘A’].mean())

print(“Thank you for Reading This post! See you soon! Bye o/”)

Colab File link:)

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