Pandas

1. What is Pandas?

Pandas is a Python library built on top of NumPy. It is designed for data analysis and manipulation tasks. It provides flexible data structures to work efficiently with structured data, such as CSV files, Excel sheets, SQL tables, and more.

Installing Pandas

pip install pandas

Importing Pandas

import pandas as pd

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2. Series and DataFrames

Series (1D Data Structure)

A Series is similar to a column in a table. It consists of data and an index.

import pandas as pd

# Creating a Series
s = pd.Series([10, 20, 30, 40], index=['a', 'b', 'c', 'd'])
print(s)

Output:

a    10
b    20
c    30
d    40
dtype: int64

DataFrame (2D Data Structure)

A DataFrame is a table-like structure with rows and columns.

data = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['New York', 'London', 'Paris']
}

df = pd.DataFrame(data)
print(df)

Output:

    Name  Age      City
0  Alice   25  New York
1    Bob   30   London
2 Charlie   35    Paris

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3. Creating DataFrames

From Dictionary

df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
print(df)

From List of Lists

df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])
print(df)

From NumPy Array

import numpy as np

df = pd.DataFrame(np.array([[1, 2], [3, 4]]), columns=['A', 'B'])
print(df)

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4. Reading and Writing Data

Reading CSV Files

df = pd.read_csv('data.csv')

Writing to CSV

df.to_csv('output.csv', index=False)

Reading Excel Files

df = pd.read_excel('data.xlsx', sheet_name='Sheet1')

Writing to Excel

df.to_excel('output.xlsx', index=False)

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5. Data Types and Missing Values

Checking Data Types

print(df.dtypes)

Handling Missing Values

# Fill missing values with a specific value
df.fillna(0, inplace=True)

# Drop rows with missing values
df.dropna(inplace=True)

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6. Indexing Methods: loc, iloc

Using loc (Label-based Indexing)

print(df.loc[0, 'Name'])  # Fetch value from row 0 and 'Name' column

Using iloc (Integer-based Indexing)

print(df.iloc[0, 1])  # Fetch value from row 0, column 1

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7. Boolean Indexing

Boolean indexing allows filtering rows based on conditions.

df[df['Age'] > 30]

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8. Selection Based on Conditions

Filtering Data

filtered_df = df[df['City'] == 'New York']
print(filtered_df)

Using Multiple Conditions

df[(df['Age'] > 25) & (df['City'] == 'New York')]

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9. Adding and Deleting Columns

Adding a New Column

df['Salary'] = [50000, 60000, 70000]

Deleting a Column

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

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10. Handling Missing Data

Filling Missing Values

df.fillna(value=0, inplace=True)

Dropping Missing Values

df.dropna(inplace=True)

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11. Grouping and Aggregation

Grouping Data

grouped = df.groupby('City').mean()
print(grouped)

Aggregation

df.groupby('City')['Age'].agg(['mean', 'max', 'min'])

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12. Merging and Joining DataFrames

Merging (Similar to SQL JOIN)

df1 = pd.DataFrame({'ID': [1, 2, 3], 'Name': ['Alice', 'Bob', 'Charlie']})
df2 = pd.DataFrame({'ID': [1, 2, 4], 'Score': [90, 80, 85]})

merged_df = pd.merge(df1, df2, on='ID', how='inner')  # Inner Join
print(merged_df)

Concatenation

df3 = pd.concat([df1, df2], axis=0)  # Stacking rows
df4 = pd.concat([df1, df2], axis=1)  # Stacking columns

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Conclusion

This tutorial covers the essential functionalities of Pandas, including creating DataFrames, reading and writing data, indexing, filtering, grouping, and merging data. Pandas is an incredibly powerful tool for data analysis, and mastering these concepts will help you work efficiently with structured data in Python.