Welcome to the Pandas for Data Science repository! This course is designed to take you from beginner to proficient in using Pandas, the powerful data manipulation library in Python. Whether you're just starting your data science journey or looking to sharpen your skills, this repository contains all the resources
GPL-3.0 License
Week 1: Understanding Data Science and Pandas
Week 2: Setting Up the Environment
import pandas as pd
Week 3: Introduction to Pandas Series
s = pd.Series([1, 2, 3, 4])
s = pd.Series({'a': 1, 'b': 2, 'c': 3})
Week 4: Accessing and Modifying Series
s[0] = 10
Week 5: Understanding Series Attributes
index
, values
, dtype
, name
len()
to Get Series LengthWeek 6: Common Series Methods
.sum()
, .mean()
, .min()
, .max()
.describe()
for Quick Overview.str
Accessor
s = pd.Series(['apple', 'banana', 'cherry'])
s.str.upper()
Week 7: Handling Missing Data in Series
isnull()
, notnull()
fillna()
dropna()
Week 8: Vectorized Operations and Functions
apply()
, map()
, applymap()
Week 9: What is a DataFrame?
df = pd.DataFrame({
'A': [1, 2, 3],
'B': ['a', 'b', 'c']
})
Week 10: DataFrame Attributes and Basic Operations
shape
, columns
, index
, dtypes
.head()
, .tail()
, .info()
, .describe()
df['C'] = [4, 5, 6]
Week 11: Basic Indexing and Slicing
.loc[]
.iloc[]
Week 12: Advanced DataFrame Selection
Week 13: Handling Missing Data in DataFrames
Week 14: Data Type Conversion and Transformation
astype()
.str
Methods in DataFramesWeek 15: Grouping DataFrames
groupby()
Week 16: Merging and Joining DataFrames
merge()
and join()
Methodsconcat()
Week 17: Time Series Basics
Week 18: Time Series Operations
Week 19: Introduction to Visualization
Week 20: Advanced Visualization Techniques
Week 21: Performance Optimization Techniques
Week 22: Writing Clean and Maintainable Code
Special Thanks to the Contributors