Introduction to Pandas

Pandas is a powerful Python library for data manipulation and analysis. It provides data structures like Series and DataFrame to work efficiently with structured data.

Installing Pandas

To use Pandas, install it via pip:

pip install pandas

Core Data Structures

1. Series

A Series is a one-dimensional labeled array.

import pandas as pd

# Creating a Series
data = [10, 20, 30]
series = pd.Series(data, index=["a", "b", "c"])

print(series)
# Output:
# a    10
# b    20
# c    30
# dtype: int64

2. DataFrame

A DataFrame is a two-dimensional labeled data structure.

# Creating a DataFrame
data = {
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [25, 30, 35],
    "City": ["New York", "Los Angeles", "Chicago"]
}
df = pd.DataFrame(data)

print(df)
# Output:
#       Name  Age         City
# 0    Alice   25     New York
# 1      Bob   30  Los Angeles
# 2  Charlie   35      Chicago

Reading and Writing Data

Reading Data

Pandas supports reading data from various file formats:

• CSV:

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

• Excel:

  df = pd.read_excel("data.xlsx")
  

• JSON:

  df = pd.read_json("data.json")
  

Writing Data

You can write a DataFrame to a file:

• CSV:

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

• Excel:

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

Exploring Data

# Display first few rows
print(df.head())

# Display last few rows
print(df.tail())

# Summary of the DataFrame
print(df.info())

# Statistical summary
print(df.describe())

Selecting and Filtering Data

Selecting Columns

# Single column
print(df["Name"])

# Multiple columns
print(df[["Name", "City"]])

Filtering Rows

# Rows where Age > 30
print(df[df["Age"] > 30])

# Rows where City is Chicago
print(df[df["City"] == "Chicago"])

Using Conditions

# Multiple conditions
print(df[(df["Age"] > 25) & (df["City"] == "New York")])

Modifying Data

Adding a Column

df["Salary"] = [50000, 60000, 70000]
print(df)

Updating Values

df.loc[0, "Age"] = 26
print(df)

Removing Columns and Rows

# Remove a column
df = df.drop("Salary", axis=1)

# Remove a row
df = df.drop(0, axis=0)

Grouping and Aggregating Data

Grouping

grouped = df.groupby("City")
print(grouped["Age"].mean())

Aggregating

print(df.groupby("City").agg({"Age": ["mean", "max"]}))

Merging and Joining DataFrames

Merging

df1 = pd.DataFrame({"ID": [1, 2], "Name": ["Alice", "Bob"]})
df2 = pd.DataFrame({"ID": [1, 2], "Score": [85, 90]})

merged = pd.merge(df1, df2, on="ID")
print(merged)

Concatenating

df1 = pd.DataFrame({"Name": ["Alice", "Bob"]})
df2 = pd.DataFrame({"Name": ["Charlie", "David"]})

concat = pd.concat([df1, df2])
print(concat)

Handling Missing Data

Identifying Missing Data

print(df.isnull())
print(df.isnull().sum())

Filling Missing Data

df["Age"].fillna(30, inplace=True)

Dropping Missing Data

df.dropna(inplace=True)

Practical Examples

Calculate Average Salary

print("Average Salary:", df["Salary"].mean())

Count Unique Cities

print("Unique Cities:", df["City"].nunique())

Sort by Age

df = df.sort_values(by="Age", ascending=False)
print(df)

Practice Exercises

1. Read a CSV:
   • Load a CSV file into a DataFrame and display the first 5 rows.
2. Filter Data:
   • Find all rows where the age is greater than 30.
3. Group and Aggregate:
   • Group data by a column and calculate the average of another column.
4. Handle Missing Data:
   • Identify missing values in a dataset and fill them with a default value.
5. Merge DataFrames:
   • Merge two DataFrames on a common column.

Pandas is an essential library for anyone working with data in Python. Mastering its features will enable you to handle, analyze, and visualize data efficiently.