“Python for Data Analysis” can mean two things:

You want to learn how to use Python for data analysis, or
You are referring to the popular book Python for Data Analysis by Wes McKinney (Pandas creator).

Below I cover both. If you want something different, tell me!

✅ 1. Using Python for Data Analysis — Quick Guide

Core Libraries

Task	Library	Notes
Data manipulation	pandas	Tables, CSV/Excel, grouping, merging
Numerical computing	numpy	Arrays, math, performance
Visualization	matplotlib, seaborn, plotly	Static + interactive charts
Machine learning	scikit-learn	Regression, classification, clustering
Big data	pyspark, dask	Scales beyond RAM

📊 Basic Workflow Example

Load data

import pandas as pd

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

Clean data

df = df.dropna()                     # remove missing values
df["age"] = df["age"].astype(int)   # type conversion

Exploratory analysis

print(df.describe())
print(df.groupby("gender")["income"].mean())

Visualization

import seaborn as sns
import matplotlib.pyplot as plt

sns.histplot(df["income"], kde=True)
plt.show()

Modeling

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

X = df[["age", "education_years"]]
y = df["income"]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

model = LinearRegression()
model.fit(X_train, y_train)

print(model.score(X_test, y_test))

📚 2. About the book Python for Data Analysis

Written by Wes McKinney, creator of pandas.
Covers:

Pandas fundamentals
Data wrangling
Merging, reshaping, time series
NumPy basics
Practical datasets

If you want a summary, table of contents, or exercises, just ask.

Great! Here’s more depth, including step-by-step workflows, intermediate/advanced techniques, and practical examples you can use immediately.

🔥 1. Full Data Analysis Workflow in Python

A complete project usually follows these steps:

Import libraries
Load data
Inspect the dataset
Clean the data
Analyze patterns
Visualize results
Build predictive models (optional)
Report insights

Below is each step with practical code.

📌 2. Import Necessary Libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

📌 3. Load Data From Different Sources

CSV

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

Excel

df = pd.read_excel("file.xlsx", sheet_name="Sheet1")

SQL

import sqlite3

conn = sqlite3.connect("database.db")
df = pd.read_sql("SELECT * FROM customers", conn)

JSON

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

📌 4. Inspect Data (Exploratory Data Analysis)

df.head()
df.tail()
df.info()
df.describe()
df.nunique()
df.shape

Check missing values:

df.isna().sum()

📌 5. Data Cleaning Techniques

Remove duplicates

df = df.drop_duplicates()

Replace missing values

df['salary'] = df['salary'].fillna(df['salary'].median())

Apply transformations

df['date'] = pd.to_datetime(df['date'])
df['price_log'] = np.log(df['price'] + 1)

Filter rows

df = df[df['age'] > 25]

📌 6. Data Visualization (More Examples)

Histogram

sns.histplot(df['age'])
plt.show()

Boxplot (detect outliers)

sns.boxplot(x=df['salary'])
plt.show()

Scatter plot

sns.scatterplot(x='age', y='salary', data=df)
plt.show()

Heatmap (correlation)

sns.heatmap(df.corr(numeric_only=True), annot=True, cmap='coolwarm')
plt.show()

📌 7. Feature Engineering

Create new columns

df['income_per_year'] = df['salary'] * 12

Binning

df['age_group'] = pd.cut(df['age'], bins=[0,18,35,60,100], labels=['child','young','adult','senior'])

One-hot encoding (for ML)

df = pd.get_dummies(df, columns=['gender', 'city'])

📌 8. Machine Learning Examples

a) Train/Test Split

from sklearn.model_selection import train_test_split

X = df[['age', 'experience']]
y = df['salary']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

b) Linear Regression

from sklearn.linear_model import LinearRegression

model = LinearRegression()
model.fit(X_train, y_train)

print("Score:", model.score(X_test, y_test))

c) Classification (Logistic Regression)

from sklearn.linear_model import LogisticRegression

clf = LogisticRegression()
clf.fit(X_train, y_train)

d) Random Forest

from sklearn.ensemble import RandomForestClassifier

rf = RandomForestClassifier()
rf.fit(X_train, y_train)

📌 9. Save & Export Results

Save to CSV

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

Save model

import joblib
joblib.dump(model, "model.pkl")

🎁 10. Real Project Example

If you upload a dataset, I can produce:

✔ Complete analysis
✔ Plots
✔ Statistical insights
✔ ML model
✔ Code in Python or Jupyter Notebook

If you want, I can also show:

➡ A full end-to-end case study
➡ Advanced data cleaning
➡ Time-series analysis
➡ Deep learning with TensorFlow or PyTorch
➡ SQL + Python workflows

Just tell me “Show an example project” or send your data!

SHOPFORUS

Python for data analysis