👩‍💻 A simple cheat sheet for working with Pandas functions for Data Science!

Here's a simple cheat sheet for working with Pandas functions in Data Science:

Basic Data Structures

Series: A one-dimensional labeled array capable of holding any data type.

import pandas as pd
s = pd.Series([3, -5, 7, 4], index=['a', 'b', 'c', 'd'])

DataFrame: A two-dimensional labeled data structure with columns of potentially different types.

data = {'Country': ['Belgium', 'India', 'Brazil'], 'Capital': ['Brussels', 'New Delhi', 'Brasilia'], 'Population': [11190846, 1303171035, 207847528]}
df = pd.DataFrame(data, columns=['Country', 'Capital', 'Population'])

Data Manipulation

Selecting Data:

df['Country']  # Selects the 'Country' column
df.loc[:, 'Country':'Population']  # Selects a range of columns

Filtering Data:

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df[df['Population'] > 10000000]  # Filters rows where Population > 10000000

Adding Data:

df['Area'] = [30528, 3287263, 8515767]  # Adds a new column 'Area'

Data Cleaning

Handling Missing Values:

df.fillna(0)  # Replaces missing values with 0
df.dropna()  # Drops rows with missing values

Dropping Columns:

df.drop(columns=['Area'])  # Drops the 'Area' column

Data Analysis

Descriptive Statistics:

df.describe()  # Generates descriptive statistics

Grouping Data:

df.groupby('Country').sum()  # Groups data by 'Country' and sums other columns

Data Visualization

Plotting Data:

import matplotlib.pyplot as plt
df.plot(kind='bar', x='Country', y='Population')
plt.show()

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