Getting Started with Pandas: Installation, DataFrames, and Basic Data Analysis in Python

Pandas is a powerful Python data‑analysis library widely used for data cleaning, processing, and analysis. It provides convenient data structures and tools that simplify handling tabular data.

Installation

Install Pandas via pip:

<code>pip install pandas</code>

Importing Pandas

After installation, import the library using the common alias pd :

<code>import pandas as pd</code>

Creating a DataFrame

A DataFrame is Pandas' primary data structure, similar to an Excel sheet or SQL table. It can be created from lists, dictionaries, CSV files, etc.

From a List

<code>data = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['New York', 'Los Angeles', 'Chicago']
}

df = pd.DataFrame(data)
print(df)
</code>

Output:

<code>      Name  Age        City
0    Alice   25    New York
1      Bob   30  Los Angeles
2  Charlie   35     Chicago
</code>

From a CSV File

Assuming a file data.csv with the following content:

<code>Name,Age,City
Alice,25,New York
Bob,30,Los Angeles
Charlie,35,Chicago
</code>

Read it with:

<code>df = pd.read_csv('data.csv')
print(df)
</code>

Output:

<code>      Name  Age        City
0    Alice   25    New York
1      Bob   30  Los Angeles
2  Charlie   35     Chicago
</code>

Basic DataFrame Inspection

df.head() – view the first few rows.

df.tail() – view the last few rows.

df.columns – list column names.

df.dtypes – show data types of each column.

Data Selection and Filtering

Select a single column: df['Name']

Select multiple columns: df[['Name', 'Age']]

Conditional filtering: filtered_df = df[df['Age'] > 30]

Sorting

Sort by a column using sort_values :

<code>sorted_df = df.sort_values(by='Age', ascending=False)
print(sorted_df)
</code>

Grouping

Group data and compute aggregates with groupby :

<code>grouped_df = df.groupby('City').mean()
print(grouped_df)
</code>

Missing‑Value Handling

Check for missing values: df.isnull()

Fill missing values: df['Age'] = df['Age'].fillna(0)

Practical Example: Stock Data Analysis

Given a CSV stock_data.csv containing daily stock prices, read and analyze it:

<code>df = pd.read_csv('stock_data.csv')
print(df)
</code>

Calculate daily percentage change:

<code>df['Change'] = df['Close'].pct_change() * 100
print(df)
</code>

Plot the closing‑price trend (requires matplotlib ):

<code>import matplotlib.pyplot as plt

plt.figure(figsize=(10, 5))
plt.plot(df['Date'], df['Close'], marker='o')
plt.title('Stock Closing Price Trend')
plt.xlabel('Date')
plt.ylabel('Closing Price')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()
</code>

Conclusion

The article demonstrated how to use Pandas for data analysis in Python, covering installation, DataFrame creation, basic inspection, selection, filtering, sorting, grouping, missing‑value handling, and a real‑world stock‑price analysis case.