CookieSensei

CookieSensei Home Learn Free Workshops Live Guidance About FAQ Contact Launch Lab

Learn/Programming & AI/Thinking Like a Programmer/Exploratory Data Analysis

Thinking Like a Programmer•Interactive notebook lesson•Free

pandas complete guide

No account is required. Learn the material, try the examples, and mark it complete locally when you are ready to move on.

Complete Pandas Guide (Beginner → Advanced)

This notebook is a comprehensive guide to Pandas, the most widely used Python library for data analysis.

Topics covered:

Introduction to Pandas
Series
DataFrames
Data Loading (CSV, Excel, JSON)
Inspecting Data
Indexing & Selection
Filtering Data
Handling Missing Values
Sorting
GroupBy Operations
Aggregations
Merging & Joining
Pivot Tables
Time Series
String Operations
Applying Functions
Window Operations
Performance Tips
Visualization
Real Data Analysis Example

Run the code cells sequentially to explore each concept.

Codepython


import pandas as pd
import numpy as np

print("Pandas version:", pd.__version__)

Output

Pandas version: 2.1.4

Codepython


import pandas as pd

data = {
    "name": ["Alice","Bob","Charlie"],
    "age": [25,30,35],
    "city": ["NY","London","Paris"]
}

df = pd.DataFrame(data)
print(df)

Output

      name  age    city
0    Alice   25      NY
1      Bob   30  London
2  Charlie   35   Paris

Codepython


print(df.head())
print(df.tail())
print(df.info())
print(df.describe())

Output

      name  age    city
0    Alice   25      NY
1      Bob   30  London
2  Charlie   35   Paris
      name  age    city
0    Alice   25      NY
1      Bob   30  London
2  Charlie   35   Paris
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 3 columns):
 #   Column  Non-Null Count  Dtype 
---  ------  --------------  ----- 
 0   name    3 non-null      object
 1   age     3 non-null      int64 
 2   city    3 non-null      object
dtypes: int64(1), object(2)
memory usage: 200.0+ bytes
None
        age
count   3.0
mean   30.0
std     5.0
min    25.0
25%    27.5
50%    30.0
75%    32.5
max    35.0

Codepython


df_missing = pd.DataFrame({
    "A":[1,2,None],
    "B":[4,None,6]
})

print(df_missing)
print(df_missing.isna())
print(df_missing.fillna(0))

Output

     A    B
0  1.0  4.0
1  2.0  NaN
2  NaN  6.0
       A      B
0  False  False
1  False   True
2   True  False
     A    B
0  1.0  4.0
1  2.0  0.0
2  0.0  6.0

Codepython


data = {
    "team":["A","A","B","B","B"],
    "points":[10,20,30,40,50]
}

df_group = pd.DataFrame(data)

grouped = df_group.groupby("team").mean()
print(grouped)

Output

      points
team        
A       15.0
B       40.0

Codepython


left = pd.DataFrame({"id":[1,2,3],"name":["Alice","Bob","Charlie"]})
right = pd.DataFrame({"id":[1,2,3],"salary":[50000,60000,70000]})

merged = pd.merge(left,right,on="id")
print(merged)

Output

   id     name  salary
0   1    Alice   50000
1   2      Bob   60000
2   3  Charlie   70000

Codepython


data = {
    "city":["NY","NY","London","London"],
    "year":[2020,2021,2020,2021],
    "sales":[100,120,90,130]
}

df_pivot = pd.DataFrame(data)
print(df_pivot)

pivot = df_pivot.pivot_table(values="sales",index="city",columns="year")
print(pivot)

Output

     city  year  sales
0      NY  2020    100
1      NY  2021    120
2  London  2020     90
3  London  2021    130
year     2020   2021
city                
London   90.0  130.0
NY      100.0  120.0

Codepython


dates = pd.date_range("2023-01-01", periods=5)

ts = pd.Series(range(5), index=dates)
print(ts)

Output

2023-01-01    0
2023-01-02    1
2023-01-03    2
2023-01-04    3
2023-01-05    4
Freq: D, dtype: int64

Codepython


names = pd.Series(["alice","bob","charlie"])

print(names.str.upper())
print(names.str.contains("a"))

Output

0      ALICE
1        BOB
2    CHARLIE
dtype: object
0     True
1    False
2     True
dtype: bool

Codepython


df["age_plus_10"] = df["age"].apply(lambda x: x+10)
print(df)

Output

      name  age    city  salary  age_plus_10
0    Alice   25      NY   50000           35
1      Bob   30  London   60000           40
2  Charlie   35   Paris   70000           45

Codepython


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

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

print(loaded)

Output

      name  age    city  salary  age_plus_10
0    Alice   25      NY   50000           35
1      Bob   30  London   60000           40
2  Charlie   35   Paris   70000           45

Codepython


data = {
    "product":["A","B","A","B","C","A"],
    "sales":[100,200,150,180,90,220]
}

df_sales = pd.DataFrame(data)

summary = df_sales.groupby("product")["sales"].sum()

print(summary)

Output

product
A    470
B    380
C     90
Name: sales, dtype: int64

Complete Pandas Guide (Beginner → Advanced)

Creating a Series

Exercise

Series with Index

Exercise

Creating DataFrames

Exercise

Inspecting Data

Exercise

Selecting Columns

Exercise

Row Selection with loc

Exercise

Row Selection with iloc

Exercise

Filtering Data

Exercise

Adding Columns

Exercise

Dropping Columns

Exercise

Handling Missing Data

Exercise

Sorting Data

Exercise

GroupBy Example

Exercise

Aggregations

Exercise

Merging DataFrames

Exercise

Concatenation

Exercise

Pivot Tables

Exercise

Time Series

Exercise

String Operations

Exercise

Apply Functions

Exercise

Rolling Window

Exercise

Basic Visualization

Exercise

Saving Data

Exercise

Mini Data Analysis Example

Exercise

Finished this lesson?