Master Pandas in Python: Install, Explore, Analyze, and Visualize Data Quickly

Python Overview

Python is a powerful, easy‑to‑learn interpreted language with rich data structures, cross‑platform support, and extensive use in data science, AI, and big‑data fields.

Pandas Introduction

Pandas is a third‑party Python library for flexible data manipulation and analysis, handling numeric, time‑series, and text data. It was created by Wes McKinney in 2008 and is now at version 1.2.1.

Installation

pip install pandas matplotlib
# or use a domestic mirror
pip install pandas matplotlib -i https://pypi.tuna.tsinghua.edu.cn/simple

import pandas as pd

Loading a Dataset

Read an Excel or CSV file into a DataFrame (df):

df = pd.read_excel('https://www.gairuo.com/file/data/dataset/team.xlsx')

df.head(), df.tail(), df.sample(5) display sample rows.

Basic Operations

Read from Excel, CSV, SQL, clipboard, etc.

Merge, split, clean (deduplicate, fill missing, handle outliers).

Indexing, large‑scale handling, column insertion, flexible queries.

Group‑by, aggregation, pivot‑like calculations.

Transpose, stack/unstack.

Add columns, compute totals and averages.

Statistical functions: mean, describe, corr, count, max, min, median, std, var, mode.

Data Selection

Column selection:

df['Q1']
df.Q1

Row selection by label or position:

df[df.index == 'Liver']
df[0:3]
df.iloc[:10, :]

Conditional filtering:

df[df.Q1 > 90]
df[(df['Q1'] > 90) & (df['team'] == 'C')]

Sorting

df.sort_values(by='Q1')
df.sort_values(by='Q1', ascending=False)
df.sort_values(['team','Q1'], ascending=[True, False])

Grouping and Aggregation

df.groupby('team').sum()
df.groupby('team').mean()
df.groupby('team').agg({'Q1':'sum','Q2':'count','Q3':'mean','Q4':'max'})

Transformation

df.groupby('team').sum().T

Adding Columns

df['one'] = 1
df['total'] = df['Q1'] + df['Q2'] + df['Q3'] + df['Q4']
df['avg'] = df['total'] / 4

Visualization

Quick plots using Pandas built‑in .plot() which leverages Matplotlib:

df['Q1'].plot()
df.loc['Ben','Q1':'Q4'].plot()
df.loc['Ben','Q1':'Q4'].plot.bar()
df.groupby('team').sum().T.plot()
df.groupby('team').count().Q1.plot.pie()

Export

df.to_excel('team-done.xlsx')
df.to_csv('team-done.csv')