A Beginner’s Guide to Pandas for Data Analysis

02215

Welcome to the world of data analysis with Pandas! If you’re new to programming or data analytics, don’t worry—this guide is designed to be simple, friendly, and hands-on. Pandas is a powerful Python library that makes working with data as easy as playing with a spreadsheet—but way more fun! By the end of this article, you’ll understand the essentials of Pandas, from loading data to analyzing it, and we’ll even build a small project together, complete with a simple graph. Let’s dive in!

What is Pandas?

Pandas is a Python library that helps you work with data in tables (called DataFrames) and lists (called Series). Think of it as a supercharged version of Excel or Google Sheets, where you can clean, explore, and analyze data with code. Whether you’re handling sales figures, survey results, or anything tabular, Pandas has your back.

To use Pandas, you’ll need Python installed on your computer. If you don’t have it yet, download it from python.org, then install Pandas by running this in your terminal or command prompt: 

pip install pandas
pip install pandas
pip install pandas

Enter fullscreen mode Exit fullscreen mode

1. Getting Started with Pandas

Let’s kick things off by importing Pandas and creating some basic data structures. 

<span># Import Pandas with the nickname 'pd' (a common shortcut) </span><span>import</span> <span>pandas</span> <span>as</span> <span>pd</span>
<span># Create a Series (a single column of data) </span><span>series</span> <span>=</span> <span>pd</span><span>.</span><span>Series</span><span>([</span><span>10</span><span>,</span> <span>20</span><span>,</span> <span>30</span><span>],</span> <span>index</span><span>=</span><span>[</span><span>'</span><span>a</span><span>'</span><span>,</span> <span>'</span><span>b</span><span>'</span><span>,</span> <span>'</span><span>c</span><span>'</span><span>])</span>
<span># Create a DataFrame (a table with rows and columns) </span><span>data</span> <span>=</span> <span>{</span><span>'</span><span>Name</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>Alice</span><span>'</span><span>,</span> <span>'</span><span>Bob</span><span>'</span><span>],</span> <span>'</span><span>Age</span><span>'</span><span>:</span> <span>[</span><span>25</span><span>,</span> <span>30</span><span>]}</span>
<span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>(</span><span>data</span><span>)</span>
<span># Show the results </span><span>print</span><span>(</span><span>"</span><span>Series:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>series</span><span>)</span>
<span>print</span><span>(</span><span>"</span><span>\n</span><span>DataFrame:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>)</span>
<span># Import Pandas with the nickname 'pd' (a common shortcut) </span><span>import</span> <span>pandas</span> <span>as</span> <span>pd</span>

<span># Create a Series (a single column of data) </span><span>series</span> <span>=</span> <span>pd</span><span>.</span><span>Series</span><span>([</span><span>10</span><span>,</span> <span>20</span><span>,</span> <span>30</span><span>],</span> <span>index</span><span>=</span><span>[</span><span>'</span><span>a</span><span>'</span><span>,</span> <span>'</span><span>b</span><span>'</span><span>,</span> <span>'</span><span>c</span><span>'</span><span>])</span>

<span># Create a DataFrame (a table with rows and columns) </span><span>data</span> <span>=</span> <span>{</span><span>'</span><span>Name</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>Alice</span><span>'</span><span>,</span> <span>'</span><span>Bob</span><span>'</span><span>],</span> <span>'</span><span>Age</span><span>'</span><span>:</span> <span>[</span><span>25</span><span>,</span> <span>30</span><span>]}</span>
<span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>(</span><span>data</span><span>)</span>

<span># Show the results </span><span>print</span><span>(</span><span>"</span><span>Series:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>series</span><span>)</span>
<span>print</span><span>(</span><span>"</span><span>\n</span><span>DataFrame:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>)</span>
# Import Pandas with the nickname 'pd' (a common shortcut) import pandas as pd

# Create a Series (a single column of data) series = pd.Series([10, 20, 30], index=['a', 'b', 'c'])

# Create a DataFrame (a table with rows and columns) data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}
df = pd.DataFrame(data)

# Show the results print("Series:")
print(series)
print("\nDataFrame:")
print(df)

Enter fullscreen mode Exit fullscreen mode

Breakdown:

  • Importing Pandas: import pandas as pd lets us use Pandas functions with the shorthand pd.
  • Series: A Series is like a labeled list. Here, we gave it numbers (10, 20, 30) with labels (‘a’, ‘b’, ‘c’).
  • DataFrame: A DataFrame is a table. We made one with two columns: ‘Name’ and ‘Age’.
  • Output: The Series shows values with their indices, and the DataFrame looks like a neat table.

2. Loading and Saving Data

Data analysts often work with files like CSV or Excel. Pandas makes this a breeze. 

<span># Create a small DataFrame </span><span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>A</span><span>'</span><span>:</span> <span>[</span><span>1</span><span>,</span> <span>2</span><span>],</span> <span>'</span><span>B</span><span>'</span><span>:</span> <span>[</span><span>3</span><span>,</span> <span>4</span><span>]})</span>
<span># Save it to a CSV file </span><span>df</span><span>.</span><span>to_csv</span><span>(</span><span>'</span><span>my_data.csv</span><span>'</span><span>,</span> <span>index</span><span>=</span><span>False</span><span>)</span>  <span># 'index=False' skips row numbers </span>
<span># Load it back </span><span>loaded_df</span> <span>=</span> <span>pd</span><span>.</span><span>read_csv</span><span>(</span><span>'</span><span>my_data.csv</span><span>'</span><span>)</span>
<span>print</span><span>(</span><span>"</span><span>Loaded DataFrame:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>loaded_df</span><span>)</span>
<span># Create a small DataFrame </span><span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>A</span><span>'</span><span>:</span> <span>[</span><span>1</span><span>,</span> <span>2</span><span>],</span> <span>'</span><span>B</span><span>'</span><span>:</span> <span>[</span><span>3</span><span>,</span> <span>4</span><span>]})</span>

<span># Save it to a CSV file </span><span>df</span><span>.</span><span>to_csv</span><span>(</span><span>'</span><span>my_data.csv</span><span>'</span><span>,</span> <span>index</span><span>=</span><span>False</span><span>)</span>  <span># 'index=False' skips row numbers </span>
<span># Load it back </span><span>loaded_df</span> <span>=</span> <span>pd</span><span>.</span><span>read_csv</span><span>(</span><span>'</span><span>my_data.csv</span><span>'</span><span>)</span>
<span>print</span><span>(</span><span>"</span><span>Loaded DataFrame:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>loaded_df</span><span>)</span>
# Create a small DataFrame df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})

# Save it to a CSV file df.to_csv('my_data.csv', index=False)  # 'index=False' skips row numbers 
# Load it back loaded_df = pd.read_csv('my_data.csv')
print("Loaded DataFrame:")
print(loaded_df)

Enter fullscreen mode Exit fullscreen mode

Breakdown:

  • Saving: to_csv() writes the DataFrame to a file named my_data.csv.
  • Loading: read_csv() reads the file back into a DataFrame.
  • Other Formats: You can also use pd.read_excel(), pd.read_json(), or df.to_excel() for different file types.

3. Exploring Your Data

Before analyzing, you need to know what’s in your data. 

<span># Sample DataFrame </span><span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>Name</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>Alice</span><span>'</span><span>,</span> <span>'</span><span>Bob</span><span>'</span><span>],</span> <span>'</span><span>Age</span><span>'</span><span>:</span> <span>[</span><span>25</span><span>,</span> <span>30</span><span>]})</span>
<span># See the first row </span><span>print</span><span>(</span><span>"</span><span>First row:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>.</span><span>head</span><span>(</span><span>1</span><span>))</span>
<span># Check structure </span><span>print</span><span>(</span><span>"</span><span>\n</span><span>Info:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>.</span><span>info</span><span>())</span>
<span># Get stats for numbers </span><span>print</span><span>(</span><span>"</span><span>\n</span><span>Stats:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>.</span><span>describe</span><span>())</span>
<span># Sample DataFrame </span><span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>Name</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>Alice</span><span>'</span><span>,</span> <span>'</span><span>Bob</span><span>'</span><span>],</span> <span>'</span><span>Age</span><span>'</span><span>:</span> <span>[</span><span>25</span><span>,</span> <span>30</span><span>]})</span>

<span># See the first row </span><span>print</span><span>(</span><span>"</span><span>First row:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>.</span><span>head</span><span>(</span><span>1</span><span>))</span>

<span># Check structure </span><span>print</span><span>(</span><span>"</span><span>\n</span><span>Info:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>.</span><span>info</span><span>())</span>

<span># Get stats for numbers </span><span>print</span><span>(</span><span>"</span><span>\n</span><span>Stats:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>.</span><span>describe</span><span>())</span>
# Sample DataFrame df = pd.DataFrame({'Name': ['Alice', 'Bob'], 'Age': [25, 30]})

# See the first row print("First row:")
print(df.head(1))

# Check structure print("\nInfo:")
print(df.info())

# Get stats for numbers print("\nStats:")
print(df.describe())

Enter fullscreen mode Exit fullscreen mode

Breakdown:

  • head(1): Shows the first row (you can change the number).
  • info(): Lists columns, their data types (e.g., ‘object’ for text, ‘int64’ for numbers), and if anything’s missing.
  • describe(): Gives stats like average and max, but only for numeric columns like ‘Age’.

4. Selecting Data

Pandas lets you pick exactly what you need. 

<span># Sample DataFrame </span><span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>Name</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>Alice</span><span>'</span><span>,</span> <span>'</span><span>Bob</span><span>'</span><span>],</span> <span>'</span><span>Age</span><span>'</span><span>:</span> <span>[</span><span>25</span><span>,</span> <span>30</span><span>]})</span>
<span># Pick one column </span><span>print</span><span>(</span><span>"</span><span>Names only:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>[</span><span>'</span><span>Name</span><span>'</span><span>])</span>
<span># Pick rows where Age > 25 </span><span>print</span><span>(</span><span>"</span><span>\n</span><span>People over 25:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>[</span><span>df</span><span>[</span><span>'</span><span>Age</span><span>'</span><span>]</span> <span>></span> <span>25</span><span>])</span>
<span># Sample DataFrame </span><span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>Name</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>Alice</span><span>'</span><span>,</span> <span>'</span><span>Bob</span><span>'</span><span>],</span> <span>'</span><span>Age</span><span>'</span><span>:</span> <span>[</span><span>25</span><span>,</span> <span>30</span><span>]})</span>

<span># Pick one column </span><span>print</span><span>(</span><span>"</span><span>Names only:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>[</span><span>'</span><span>Name</span><span>'</span><span>])</span>

<span># Pick rows where Age > 25 </span><span>print</span><span>(</span><span>"</span><span>\n</span><span>People over 25:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>[</span><span>df</span><span>[</span><span>'</span><span>Age</span><span>'</span><span>]</span> <span>></span> <span>25</span><span>])</span>
# Sample DataFrame df = pd.DataFrame({'Name': ['Alice', 'Bob'], 'Age': [25, 30]})

# Pick one column print("Names only:")
print(df['Name'])

# Pick rows where Age > 25 print("\nPeople over 25:")
print(df[df['Age'] > 25])

Enter fullscreen mode Exit fullscreen mode

Breakdown:

  • Column Selection: df['Name'] grabs the ‘Name’ column as a Series.
  • Row Filtering: df[df['Age'] > 25] keeps only rows where the condition is true (here, just Bob).

5. Cleaning Data

Real data is messy—Pandas helps fix it. 

<span># Import NumPy for missing values (NaN) </span><span>import</span> <span>numpy</span> <span>as</span> <span>np</span>
<span># DataFrame with missing and duplicate data </span><span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>A</span><span>'</span><span>:</span> <span>[</span><span>1</span><span>,</span> <span>np</span><span>.</span><span>nan</span><span>,</span> <span>3</span><span>],</span> <span>'</span><span>B</span><span>'</span><span>:</span> <span>[</span><span>4</span><span>,</span> <span>4</span><span>,</span> <span>5</span><span>]})</span>
<span># Fill missing values with 0 </span><span>df</span><span>[</span><span>'</span><span>A</span><span>'</span><span>]</span> <span>=</span> <span>df</span><span>[</span><span>'</span><span>A</span><span>'</span><span>].</span><span>fillna</span><span>(</span><span>0</span><span>)</span>
<span># Remove duplicate rows in 'B' </span><span>df</span> <span>=</span> <span>df</span><span>.</span><span>drop_duplicates</span><span>(</span><span>subset</span><span>=</span><span>'</span><span>B</span><span>'</span><span>)</span>
<span>print</span><span>(</span><span>"</span><span>Cleaned DataFrame:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>)</span>
<span># Import NumPy for missing values (NaN) </span><span>import</span> <span>numpy</span> <span>as</span> <span>np</span>

<span># DataFrame with missing and duplicate data </span><span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>A</span><span>'</span><span>:</span> <span>[</span><span>1</span><span>,</span> <span>np</span><span>.</span><span>nan</span><span>,</span> <span>3</span><span>],</span> <span>'</span><span>B</span><span>'</span><span>:</span> <span>[</span><span>4</span><span>,</span> <span>4</span><span>,</span> <span>5</span><span>]})</span>

<span># Fill missing values with 0 </span><span>df</span><span>[</span><span>'</span><span>A</span><span>'</span><span>]</span> <span>=</span> <span>df</span><span>[</span><span>'</span><span>A</span><span>'</span><span>].</span><span>fillna</span><span>(</span><span>0</span><span>)</span>

<span># Remove duplicate rows in 'B' </span><span>df</span> <span>=</span> <span>df</span><span>.</span><span>drop_duplicates</span><span>(</span><span>subset</span><span>=</span><span>'</span><span>B</span><span>'</span><span>)</span>
<span>print</span><span>(</span><span>"</span><span>Cleaned DataFrame:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>)</span>
# Import NumPy for missing values (NaN) import numpy as np

# DataFrame with missing and duplicate data df = pd.DataFrame({'A': [1, np.nan, 3], 'B': [4, 4, 5]})

# Fill missing values with 0 df['A'] = df['A'].fillna(0)

# Remove duplicate rows in 'B' df = df.drop_duplicates(subset='B')
print("Cleaned DataFrame:")
print(df)

Enter fullscreen mode Exit fullscreen mode

Breakdown:

  • Missing Values: np.nan is a missing value. fillna(0) replaces it with 0.
  • Duplicates: drop_duplicates() keeps only the first occurrence of repeated values in ‘B’.

6. Manipulating Data

Change your data however you like. 

<span># Sample DataFrame </span><span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>Sales</span><span>'</span><span>:</span> <span>[</span><span>100</span><span>,</span> <span>200</span><span>],</span> <span>'</span><span>Region</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>North</span><span>'</span><span>,</span> <span>'</span><span>South</span><span>'</span><span>]})</span>
<span># Add a new column </span><span>df</span><span>[</span><span>'</span><span>Tax</span><span>'</span><span>]</span> <span>=</span> <span>df</span><span>[</span><span>'</span><span>Sales</span><span>'</span><span>]</span> <span>*</span> <span>0.1</span>
<span># Sort by Sales </span><span>df</span> <span>=</span> <span>df</span><span>.</span><span>sort_values</span><span>(</span><span>'</span><span>Sales</span><span>'</span><span>)</span>
<span>print</span><span>(</span><span>"</span><span>Updated DataFrame:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>)</span>
<span># Sample DataFrame </span><span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>Sales</span><span>'</span><span>:</span> <span>[</span><span>100</span><span>,</span> <span>200</span><span>],</span> <span>'</span><span>Region</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>North</span><span>'</span><span>,</span> <span>'</span><span>South</span><span>'</span><span>]})</span>

<span># Add a new column </span><span>df</span><span>[</span><span>'</span><span>Tax</span><span>'</span><span>]</span> <span>=</span> <span>df</span><span>[</span><span>'</span><span>Sales</span><span>'</span><span>]</span> <span>*</span> <span>0.1</span>

<span># Sort by Sales </span><span>df</span> <span>=</span> <span>df</span><span>.</span><span>sort_values</span><span>(</span><span>'</span><span>Sales</span><span>'</span><span>)</span>
<span>print</span><span>(</span><span>"</span><span>Updated DataFrame:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>)</span>
# Sample DataFrame df = pd.DataFrame({'Sales': [100, 200], 'Region': ['North', 'South']})

# Add a new column df['Tax'] = df['Sales'] * 0.1

# Sort by Sales df = df.sort_values('Sales')
print("Updated DataFrame:")
print(df)

Enter fullscreen mode Exit fullscreen mode

Breakdown:

  • New Column: df['Tax'] creates a column based on a calculation.
  • Sorting: sort_values('Sales') arranges rows from low to high Sales.

7. Grouping and Aggregating

Summarize data by categories. 

<span># Sample DataFrame </span><span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>Region</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>North</span><span>'</span><span>,</span> <span>'</span><span>South</span><span>'</span><span>,</span> <span>'</span><span>North</span><span>'</span><span>],</span> <span>'</span><span>Sales</span><span>'</span><span>:</span> <span>[</span><span>100</span><span>,</span> <span>150</span><span>,</span> <span>200</span><span>]})</span>
<span># Total sales by region </span><span>sales_by_region</span> <span>=</span> <span>df</span><span>.</span><span>groupby</span><span>(</span><span>'</span><span>Region</span><span>'</span><span>)[</span><span>'</span><span>Sales</span><span>'</span><span>].</span><span>sum</span><span>()</span>
<span>print</span><span>(</span><span>"</span><span>Sales by Region:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>sales_by_region</span><span>)</span>
<span># Sample DataFrame </span><span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>Region</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>North</span><span>'</span><span>,</span> <span>'</span><span>South</span><span>'</span><span>,</span> <span>'</span><span>North</span><span>'</span><span>],</span> <span>'</span><span>Sales</span><span>'</span><span>:</span> <span>[</span><span>100</span><span>,</span> <span>150</span><span>,</span> <span>200</span><span>]})</span>

<span># Total sales by region </span><span>sales_by_region</span> <span>=</span> <span>df</span><span>.</span><span>groupby</span><span>(</span><span>'</span><span>Region</span><span>'</span><span>)[</span><span>'</span><span>Sales</span><span>'</span><span>].</span><span>sum</span><span>()</span>
<span>print</span><span>(</span><span>"</span><span>Sales by Region:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>sales_by_region</span><span>)</span>
# Sample DataFrame df = pd.DataFrame({'Region': ['North', 'South', 'North'], 'Sales': [100, 150, 200]})

# Total sales by region sales_by_region = df.groupby('Region')['Sales'].sum()
print("Sales by Region:")
print(sales_by_region)

Enter fullscreen mode Exit fullscreen mode

Breakdown:

  • groupby('Region'): Groups rows by the ‘Region’ column.
  • sum(): Adds up ‘Sales’ for each group. North: 100 + 200 = 300; South: 150.

8. Merging Data

Combine multiple datasets. 

<span># Two small DataFrames </span><span>df1</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>ID</span><span>'</span><span>:</span> <span>[</span><span>1</span><span>,</span> <span>2</span><span>],</span> <span>'</span><span>Name</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>Alice</span><span>'</span><span>,</span> <span>'</span><span>Bob</span><span>'</span><span>]})</span>
<span>df2</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>ID</span><span>'</span><span>:</span> <span>[</span><span>1</span><span>,</span> <span>3</span><span>],</span> <span>'</span><span>Score</span><span>'</span><span>:</span> <span>[</span><span>85</span><span>,</span> <span>90</span><span>]})</span>
<span># Merge them on 'ID' </span><span>merged</span> <span>=</span> <span>pd</span><span>.</span><span>merge</span><span>(</span><span>df1</span><span>,</span> <span>df2</span><span>,</span> <span>on</span><span>=</span><span>'</span><span>ID</span><span>'</span><span>,</span> <span>how</span><span>=</span><span>'</span><span>inner</span><span>'</span><span>)</span>
<span>print</span><span>(</span><span>"</span><span>Merged DataFrame:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>merged</span><span>)</span>
<span># Two small DataFrames </span><span>df1</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>ID</span><span>'</span><span>:</span> <span>[</span><span>1</span><span>,</span> <span>2</span><span>],</span> <span>'</span><span>Name</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>Alice</span><span>'</span><span>,</span> <span>'</span><span>Bob</span><span>'</span><span>]})</span>
<span>df2</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>ID</span><span>'</span><span>:</span> <span>[</span><span>1</span><span>,</span> <span>3</span><span>],</span> <span>'</span><span>Score</span><span>'</span><span>:</span> <span>[</span><span>85</span><span>,</span> <span>90</span><span>]})</span>

<span># Merge them on 'ID' </span><span>merged</span> <span>=</span> <span>pd</span><span>.</span><span>merge</span><span>(</span><span>df1</span><span>,</span> <span>df2</span><span>,</span> <span>on</span><span>=</span><span>'</span><span>ID</span><span>'</span><span>,</span> <span>how</span><span>=</span><span>'</span><span>inner</span><span>'</span><span>)</span>
<span>print</span><span>(</span><span>"</span><span>Merged DataFrame:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>merged</span><span>)</span>
# Two small DataFrames df1 = pd.DataFrame({'ID': [1, 2], 'Name': ['Alice', 'Bob']})
df2 = pd.DataFrame({'ID': [1, 3], 'Score': [85, 90]})

# Merge them on 'ID' merged = pd.merge(df1, df2, on='ID', how='inner')
print("Merged DataFrame:")
print(merged)

Enter fullscreen mode Exit fullscreen mode

Breakdown:

  • merge(): Links df1 and df2 where ‘ID’ matches.
  • how='inner': Keeps only rows where ‘ID’ exists in both (here, just ID 1).

9. Pivot Tables

Rearrange data for insights. 

<span># Sample DataFrame </span><span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>Date</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>2023-01</span><span>'</span><span>,</span> <span>'</span><span>2023-01</span><span>'</span><span>],</span> <span>'</span><span>Product</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>A</span><span>'</span><span>,</span> <span>'</span><span>B</span><span>'</span><span>],</span> <span>'</span><span>Sales</span><span>'</span><span>:</span> <span>[</span><span>100</span><span>,</span> <span>150</span><span>]})</span>
<span># Pivot to spread Products across columns </span><span>pivot</span> <span>=</span> <span>df</span><span>.</span><span>pivot</span><span>(</span><span>index</span><span>=</span><span>'</span><span>Date</span><span>'</span><span>,</span> <span>columns</span><span>=</span><span>'</span><span>Product</span><span>'</span><span>,</span> <span>values</span><span>=</span><span>'</span><span>Sales</span><span>'</span><span>)</span>
<span>print</span><span>(</span><span>"</span><span>Pivot Table:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>pivot</span><span>)</span>
<span># Sample DataFrame </span><span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>Date</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>2023-01</span><span>'</span><span>,</span> <span>'</span><span>2023-01</span><span>'</span><span>],</span> <span>'</span><span>Product</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>A</span><span>'</span><span>,</span> <span>'</span><span>B</span><span>'</span><span>],</span> <span>'</span><span>Sales</span><span>'</span><span>:</span> <span>[</span><span>100</span><span>,</span> <span>150</span><span>]})</span>

<span># Pivot to spread Products across columns </span><span>pivot</span> <span>=</span> <span>df</span><span>.</span><span>pivot</span><span>(</span><span>index</span><span>=</span><span>'</span><span>Date</span><span>'</span><span>,</span> <span>columns</span><span>=</span><span>'</span><span>Product</span><span>'</span><span>,</span> <span>values</span><span>=</span><span>'</span><span>Sales</span><span>'</span><span>)</span>
<span>print</span><span>(</span><span>"</span><span>Pivot Table:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>pivot</span><span>)</span>
# Sample DataFrame df = pd.DataFrame({'Date': ['2023-01', '2023-01'], 'Product': ['A', 'B'], 'Sales': [100, 150]})

# Pivot to spread Products across columns pivot = df.pivot(index='Date', columns='Product', values='Sales')
print("Pivot Table:")
print(pivot)

Enter fullscreen mode Exit fullscreen mode

Breakdown:

  • pivot(): Turns ‘Product’ values into columns, with ‘Sales’ as the data. One row for ‘2023-01’ with A and B as columns.

10. Time Series

Work with dates and times. 

<span># Sample DataFrame </span><span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>Date</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>2023-01-01</span><span>'</span><span>,</span> <span>'</span><span>2023-02-01</span><span>'</span><span>],</span> <span>'</span><span>Sales</span><span>'</span><span>:</span> <span>[</span><span>100</span><span>,</span> <span>200</span><span>]})</span>
<span># Convert Date to datetime </span><span>df</span><span>[</span><span>'</span><span>Date</span><span>'</span><span>]</span> <span>=</span> <span>pd</span><span>.</span><span>to_datetime</span><span>(</span><span>df</span><span>[</span><span>'</span><span>Date</span><span>'</span><span>])</span>
<span># Set Date as index </span><span>df</span><span>.</span><span>set_index</span><span>(</span><span>'</span><span>Date</span><span>'</span><span>,</span> <span>inplace</span><span>=</span><span>True</span><span>)</span>
<span># Monthly total </span><span>monthly</span> <span>=</span> <span>df</span><span>.</span><span>resample</span><span>(</span><span>'</span><span>M</span><span>'</span><span>).</span><span>sum</span><span>()</span>
<span>print</span><span>(</span><span>"</span><span>Monthly Sales:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>monthly</span><span>)</span>
<span># Sample DataFrame </span><span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>Date</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>2023-01-01</span><span>'</span><span>,</span> <span>'</span><span>2023-02-01</span><span>'</span><span>],</span> <span>'</span><span>Sales</span><span>'</span><span>:</span> <span>[</span><span>100</span><span>,</span> <span>200</span><span>]})</span>

<span># Convert Date to datetime </span><span>df</span><span>[</span><span>'</span><span>Date</span><span>'</span><span>]</span> <span>=</span> <span>pd</span><span>.</span><span>to_datetime</span><span>(</span><span>df</span><span>[</span><span>'</span><span>Date</span><span>'</span><span>])</span>

<span># Set Date as index </span><span>df</span><span>.</span><span>set_index</span><span>(</span><span>'</span><span>Date</span><span>'</span><span>,</span> <span>inplace</span><span>=</span><span>True</span><span>)</span>

<span># Monthly total </span><span>monthly</span> <span>=</span> <span>df</span><span>.</span><span>resample</span><span>(</span><span>'</span><span>M</span><span>'</span><span>).</span><span>sum</span><span>()</span>
<span>print</span><span>(</span><span>"</span><span>Monthly Sales:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>monthly</span><span>)</span>
# Sample DataFrame df = pd.DataFrame({'Date': ['2023-01-01', '2023-02-01'], 'Sales': [100, 200]})

# Convert Date to datetime df['Date'] = pd.to_datetime(df['Date'])

# Set Date as index df.set_index('Date', inplace=True)

# Monthly total monthly = df.resample('M').sum()
print("Monthly Sales:")
print(monthly)

Enter fullscreen mode Exit fullscreen mode

Breakdown:

  • to_datetime(): Makes ‘Date’ a proper date type.
  • set_index(): Uses ‘Date’ as row labels.
  • resample('M'): Groups by month (‘M’) and sums ‘Sales’.

11. SQL in Pandas

Pandas can connect to databases or mimic SQL queries. 

<span># Sample DataFrame </span><span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>id</span><span>'</span><span>:</span> <span>[</span><span>1</span><span>,</span> <span>2</span><span>],</span> <span>'</span><span>product</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>Laptop</span><span>'</span><span>,</span> <span>'</span><span>Mouse</span><span>'</span><span>],</span> <span>'</span><span>amount</span><span>'</span><span>:</span> <span>[</span><span>1000</span><span>,</span> <span>20</span><span>]})</span>
<span># Filter like SQL: WHERE amount > 50 </span><span>result</span> <span>=</span> <span>df</span><span>[</span><span>df</span><span>[</span><span>'</span><span>amount</span><span>'</span><span>]</span> <span>></span> <span>50</span><span>]</span>
<span>print</span><span>(</span><span>"</span><span>Filtered DataFrame:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>result</span><span>)</span>
<span># Sample DataFrame </span><span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>id</span><span>'</span><span>:</span> <span>[</span><span>1</span><span>,</span> <span>2</span><span>],</span> <span>'</span><span>product</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>Laptop</span><span>'</span><span>,</span> <span>'</span><span>Mouse</span><span>'</span><span>],</span> <span>'</span><span>amount</span><span>'</span><span>:</span> <span>[</span><span>1000</span><span>,</span> <span>20</span><span>]})</span>

<span># Filter like SQL: WHERE amount > 50 </span><span>result</span> <span>=</span> <span>df</span><span>[</span><span>df</span><span>[</span><span>'</span><span>amount</span><span>'</span><span>]</span> <span>></span> <span>50</span><span>]</span>
<span>print</span><span>(</span><span>"</span><span>Filtered DataFrame:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>result</span><span>)</span>
# Sample DataFrame df = pd.DataFrame({'id': [1, 2], 'product': ['Laptop', 'Mouse'], 'amount': [1000, 20]})

# Filter like SQL: WHERE amount > 50 result = df[df['amount'] > 50]
print("Filtered DataFrame:")
print(result)

Enter fullscreen mode Exit fullscreen mode

Breakdown:

  • SQL-Like: df[df['amount'] > 50] is like SELECT * FROM df WHERE amount > 50.
  • Database Option: Use pd.read_sql("SELECT * FROM table", connection) to pull from a real database (e.g., MySQL).

Project: Analyzing Store Sales with a Simple Plot

Let’s put it all together with a beginner-friendly project! We’ll analyze a small store’s sales and add a basic bar chart using Matplotlib to visualize our results.

Project Setup

  1. Install Python: Download from python.org if you haven’t.
  2. Install Pandas: Run pip install pandas in your terminal.
  3. Install Matplotlib: Run pip install matplotlib to add plotting (we’ll use it briefly).
  4. Pick an Editor: Use Notepad, VS Code, or Jupyter Notebook.
  5. Create a File: Save this code as store_sales.py or run it in a notebook.

The Code 

<span># Import Pandas for data and Matplotlib for plotting </span><span>import</span> <span>pandas</span> <span>as</span> <span>pd</span>
<span>import</span> <span>matplotlib.pyplot</span> <span>as</span> <span>plt</span>
<span># Step 1: Create our dataset </span><span>data</span> <span>=</span> <span>{</span>
    <span>'</span><span>Date</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>2023-01-01</span><span>'</span><span>,</span> <span>'</span><span>2023-01-01</span><span>'</span><span>,</span> <span>'</span><span>2023-01-02</span><span>'</span><span>,</span> <span>'</span><span>2023-01-02</span><span>'</span><span>],</span>
    <span>'</span><span>Item</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>Pen</span><span>'</span><span>,</span> <span>'</span><span>Notebook</span><span>'</span><span>,</span> <span>'</span><span>Pen</span><span>'</span><span>,</span> <span>'</span><span>Pencil</span><span>'</span><span>],</span>
    <span>'</span><span>Quantity</span><span>'</span><span>:</span> <span>[</span><span>5</span><span>,</span> <span>2</span><span>,</span> <span>3</span><span>,</span> <span>4</span><span>],</span>
    <span>'</span><span>Price</span><span>'</span><span>:</span> <span>[</span><span>1.0</span><span>,</span> <span>2.5</span><span>,</span> <span>1.0</span><span>,</span> <span>0.5</span><span>]</span>
<span>}</span>
<span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>(</span><span>data</span><span>)</span>  <span># Turn the dictionary into a table </span><span>print</span><span>(</span><span>"</span><span>Our dataset:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>)</span>
<span># Step 2: Explore the data </span><span>print</span><span>(</span><span>"</span><span>\n</span><span>First 2 rows:</span><span>"</span><span>)</span>  <span># Show first 2 rows </span><span>print</span><span>(</span><span>df</span><span>.</span><span>head</span><span>(</span><span>2</span><span>))</span>
<span>print</span><span>(</span><span>"</span><span>\n</span><span>Dataset info:</span><span>"</span><span>)</span>  <span># Check structure </span><span>print</span><span>(</span><span>df</span><span>.</span><span>info</span><span>())</span>
<span># Step 3: Calculate total sales per row </span><span>df</span><span>[</span><span>'</span><span>Total</span><span>'</span><span>]</span> <span>=</span> <span>df</span><span>[</span><span>'</span><span>Quantity</span><span>'</span><span>]</span> <span>*</span> <span>df</span><span>[</span><span>'</span><span>Price</span><span>'</span><span>]</span>  <span># New column: Quantity × Price </span><span>print</span><span>(</span><span>"</span><span>\n</span><span>With Total column:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>)</span>
<span>total_sales</span> <span>=</span> <span>df</span><span>[</span><span>'</span><span>Total</span><span>'</span><span>].</span><span>sum</span><span>()</span>  <span># Add up all totals </span><span>print</span><span>(</span><span>f</span><span>"</span><span>\n</span><span>Total sales: $</span><span>{</span><span>total_sales</span><span>}</span><span>"</span><span>)</span>
<span># Step 4: Find total quantity sold per item </span><span>item_sales</span> <span>=</span> <span>df</span><span>.</span><span>groupby</span><span>(</span><span>'</span><span>Item</span><span>'</span><span>)[</span><span>'</span><span>Quantity</span><span>'</span><span>].</span><span>sum</span><span>()</span>  <span># Total quantity per item </span><span>print</span><span>(</span><span>"</span><span>\n</span><span>Quantity sold per item:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>item_sales</span><span>)</span>
<span># Step 5: Make a simple bar chart of item sales </span><span>plt</span><span>.</span><span>bar</span><span>(</span><span>item_sales</span><span>.</span><span>index</span><span>,</span> <span>item_sales</span><span>)</span>  <span># Items on x-axis, quantities on y-axis </span><span>plt</span><span>.</span><span>title</span><span>(</span><span>'</span><span>Total Quantity Sold Per Item</span><span>'</span><span>)</span>  <span># Add a title </span><span>plt</span><span>.</span><span>xlabel</span><span>(</span><span>'</span><span>Item</span><span>'</span><span>)</span>  <span># Label for x-axis </span><span>plt</span><span>.</span><span>ylabel</span><span>(</span><span>'</span><span>Quantity Sold</span><span>'</span><span>)</span>  <span># Label for y-axis </span><span>plt</span><span>.</span><span>show</span><span>()</span>  <span># Display the plot </span>
<span># Step 6: Average sales per day </span><span>df</span><span>[</span><span>'</span><span>Date</span><span>'</span><span>]</span> <span>=</span> <span>pd</span><span>.</span><span>to_datetime</span><span>(</span><span>df</span><span>[</span><span>'</span><span>Date</span><span>'</span><span>])</span>  <span># Make Date a proper date </span><span>daily_sales</span> <span>=</span> <span>df</span><span>.</span><span>groupby</span><span>(</span><span>'</span><span>Date</span><span>'</span><span>)[</span><span>'</span><span>Total</span><span>'</span><span>].</span><span>sum</span><span>()</span>  <span># Total sales per day </span><span>print</span><span>(</span><span>"</span><span>\n</span><span>Total sales per day:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>daily_sales</span><span>)</span>
<span>avg_daily_sales</span> <span>=</span> <span>daily_sales</span><span>.</span><span>mean</span><span>()</span>  <span># Average of daily totals </span><span>print</span><span>(</span><span>f</span><span>"</span><span>\n</span><span>Average daily sales: $</span><span>{</span><span>avg_daily_sales</span><span>}</span><span>"</span><span>)</span>
<span># Step 7: Save the results </span><span>df</span><span>.</span><span>to_csv</span><span>(</span><span>'</span><span>store_sales.csv</span><span>'</span><span>,</span> <span>index</span><span>=</span><span>False</span><span>)</span>  <span># Save to a CSV file </span><span>print</span><span>(</span><span>"</span><span>\n</span><span>Data saved to </span><span>'</span><span>store_sales.csv</span><span>'</span><span>!</span><span>"</span><span>)</span>
<span># Import Pandas for data and Matplotlib for plotting </span><span>import</span> <span>pandas</span> <span>as</span> <span>pd</span>
<span>import</span> <span>matplotlib.pyplot</span> <span>as</span> <span>plt</span>

<span># Step 1: Create our dataset </span><span>data</span> <span>=</span> <span>{</span>
    <span>'</span><span>Date</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>2023-01-01</span><span>'</span><span>,</span> <span>'</span><span>2023-01-01</span><span>'</span><span>,</span> <span>'</span><span>2023-01-02</span><span>'</span><span>,</span> <span>'</span><span>2023-01-02</span><span>'</span><span>],</span>
    <span>'</span><span>Item</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>Pen</span><span>'</span><span>,</span> <span>'</span><span>Notebook</span><span>'</span><span>,</span> <span>'</span><span>Pen</span><span>'</span><span>,</span> <span>'</span><span>Pencil</span><span>'</span><span>],</span>
    <span>'</span><span>Quantity</span><span>'</span><span>:</span> <span>[</span><span>5</span><span>,</span> <span>2</span><span>,</span> <span>3</span><span>,</span> <span>4</span><span>],</span>
    <span>'</span><span>Price</span><span>'</span><span>:</span> <span>[</span><span>1.0</span><span>,</span> <span>2.5</span><span>,</span> <span>1.0</span><span>,</span> <span>0.5</span><span>]</span>
<span>}</span>
<span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>(</span><span>data</span><span>)</span>  <span># Turn the dictionary into a table </span><span>print</span><span>(</span><span>"</span><span>Our dataset:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>)</span>

<span># Step 2: Explore the data </span><span>print</span><span>(</span><span>"</span><span>\n</span><span>First 2 rows:</span><span>"</span><span>)</span>  <span># Show first 2 rows </span><span>print</span><span>(</span><span>df</span><span>.</span><span>head</span><span>(</span><span>2</span><span>))</span>
<span>print</span><span>(</span><span>"</span><span>\n</span><span>Dataset info:</span><span>"</span><span>)</span>  <span># Check structure </span><span>print</span><span>(</span><span>df</span><span>.</span><span>info</span><span>())</span>

<span># Step 3: Calculate total sales per row </span><span>df</span><span>[</span><span>'</span><span>Total</span><span>'</span><span>]</span> <span>=</span> <span>df</span><span>[</span><span>'</span><span>Quantity</span><span>'</span><span>]</span> <span>*</span> <span>df</span><span>[</span><span>'</span><span>Price</span><span>'</span><span>]</span>  <span># New column: Quantity × Price </span><span>print</span><span>(</span><span>"</span><span>\n</span><span>With Total column:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>)</span>
<span>total_sales</span> <span>=</span> <span>df</span><span>[</span><span>'</span><span>Total</span><span>'</span><span>].</span><span>sum</span><span>()</span>  <span># Add up all totals </span><span>print</span><span>(</span><span>f</span><span>"</span><span>\n</span><span>Total sales: $</span><span>{</span><span>total_sales</span><span>}</span><span>"</span><span>)</span>

<span># Step 4: Find total quantity sold per item </span><span>item_sales</span> <span>=</span> <span>df</span><span>.</span><span>groupby</span><span>(</span><span>'</span><span>Item</span><span>'</span><span>)[</span><span>'</span><span>Quantity</span><span>'</span><span>].</span><span>sum</span><span>()</span>  <span># Total quantity per item </span><span>print</span><span>(</span><span>"</span><span>\n</span><span>Quantity sold per item:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>item_sales</span><span>)</span>

<span># Step 5: Make a simple bar chart of item sales </span><span>plt</span><span>.</span><span>bar</span><span>(</span><span>item_sales</span><span>.</span><span>index</span><span>,</span> <span>item_sales</span><span>)</span>  <span># Items on x-axis, quantities on y-axis </span><span>plt</span><span>.</span><span>title</span><span>(</span><span>'</span><span>Total Quantity Sold Per Item</span><span>'</span><span>)</span>  <span># Add a title </span><span>plt</span><span>.</span><span>xlabel</span><span>(</span><span>'</span><span>Item</span><span>'</span><span>)</span>  <span># Label for x-axis </span><span>plt</span><span>.</span><span>ylabel</span><span>(</span><span>'</span><span>Quantity Sold</span><span>'</span><span>)</span>  <span># Label for y-axis </span><span>plt</span><span>.</span><span>show</span><span>()</span>  <span># Display the plot </span>
<span># Step 6: Average sales per day </span><span>df</span><span>[</span><span>'</span><span>Date</span><span>'</span><span>]</span> <span>=</span> <span>pd</span><span>.</span><span>to_datetime</span><span>(</span><span>df</span><span>[</span><span>'</span><span>Date</span><span>'</span><span>])</span>  <span># Make Date a proper date </span><span>daily_sales</span> <span>=</span> <span>df</span><span>.</span><span>groupby</span><span>(</span><span>'</span><span>Date</span><span>'</span><span>)[</span><span>'</span><span>Total</span><span>'</span><span>].</span><span>sum</span><span>()</span>  <span># Total sales per day </span><span>print</span><span>(</span><span>"</span><span>\n</span><span>Total sales per day:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>daily_sales</span><span>)</span>
<span>avg_daily_sales</span> <span>=</span> <span>daily_sales</span><span>.</span><span>mean</span><span>()</span>  <span># Average of daily totals </span><span>print</span><span>(</span><span>f</span><span>"</span><span>\n</span><span>Average daily sales: $</span><span>{</span><span>avg_daily_sales</span><span>}</span><span>"</span><span>)</span>

<span># Step 7: Save the results </span><span>df</span><span>.</span><span>to_csv</span><span>(</span><span>'</span><span>store_sales.csv</span><span>'</span><span>,</span> <span>index</span><span>=</span><span>False</span><span>)</span>  <span># Save to a CSV file </span><span>print</span><span>(</span><span>"</span><span>\n</span><span>Data saved to </span><span>'</span><span>store_sales.csv</span><span>'</span><span>!</span><span>"</span><span>)</span>
# Import Pandas for data and Matplotlib for plotting import pandas as pd
import matplotlib.pyplot as plt

# Step 1: Create our dataset data = {
    'Date': ['2023-01-01', '2023-01-01', '2023-01-02', '2023-01-02'],
    'Item': ['Pen', 'Notebook', 'Pen', 'Pencil'],
    'Quantity': [5, 2, 3, 4],
    'Price': [1.0, 2.5, 1.0, 0.5]
}
df = pd.DataFrame(data)  # Turn the dictionary into a table print("Our dataset:")
print(df)

# Step 2: Explore the data print("\nFirst 2 rows:")  # Show first 2 rows print(df.head(2))
print("\nDataset info:")  # Check structure print(df.info())

# Step 3: Calculate total sales per row df['Total'] = df['Quantity'] * df['Price']  # New column: Quantity × Price print("\nWith Total column:")
print(df)
total_sales = df['Total'].sum()  # Add up all totals print(f"\nTotal sales: ${total_sales}")

# Step 4: Find total quantity sold per item item_sales = df.groupby('Item')['Quantity'].sum()  # Total quantity per item print("\nQuantity sold per item:")
print(item_sales)

# Step 5: Make a simple bar chart of item sales plt.bar(item_sales.index, item_sales)  # Items on x-axis, quantities on y-axis plt.title('Total Quantity Sold Per Item')  # Add a title plt.xlabel('Item')  # Label for x-axis plt.ylabel('Quantity Sold')  # Label for y-axis plt.show()  # Display the plot 
# Step 6: Average sales per day df['Date'] = pd.to_datetime(df['Date'])  # Make Date a proper date daily_sales = df.groupby('Date')['Total'].sum()  # Total sales per day print("\nTotal sales per day:")
print(daily_sales)
avg_daily_sales = daily_sales.mean()  # Average of daily totals print(f"\nAverage daily sales: ${avg_daily_sales}")

# Step 7: Save the results df.to_csv('store_sales.csv', index=False)  # Save to a CSV file print("\nData saved to 'store_sales.csv'!")

Enter fullscreen mode Exit fullscreen mode

Breakdown:

  • Step 1: We created a DataFrame with sales data (dates, items, quantities, prices).
  • Step 2: Explored it with head() and info() to understand our dataset.
  • Step 3: Added a ‘Total’ column (e.g., 5 pens × $1 = $5) and summed it for overall sales ($15).
  • Step 4: Grouped by ‘Item’ to see total quantities sold (e.g., Pens: 8 units).
  • Step 5: Used Matplotlib’s plt.bar() to make a bar chart:
    • item_sales.index (item names) goes on the x-axis.
    • item_sales (quantities) goes on the y-axis.
    • Added labels and a title, then plt.show() displays it. You’ll see a window pop up with bars for each item!
  • Step 6: Calculated daily sales (Jan 1: $10, Jan 2: $5) and their average ($7.5).
  • Step 7: Saved our work to store_sales.csv.

Run this code, and you’ll see the outputs in your console plus a bar chart window showing quantities sold per item (Pens tallest at 8, Notebook shortest at 2). You’ll also get a CSV file in your folder!

Conclusion

Congratulations! You’ve just taken your first steps into the exciting world of data analysis with Pandas. From creating and manipulating DataFrames to exploring, cleaning, and visualizing data, you now have the foundational skills to tackle real-world datasets. The hands-on project showed how Pandas transforms raw numbers into meaningful insights—like total sales or item quantities—complete with a simple chart to bring it all to life. As you continue your journey, experiment with larger datasets, explore advanced features like joins or time series analysis, and let Pandas empower you to uncover stories hidden in the data. Happy analyzing!

原文链接：A Beginner’s Guide to Pandas for Data Analysis

展开阅读全文
© 版权声明
文章版权声明 1、本网站名称：拾光赋
2、本站永久网址：https://www.blogs.ink
3、本网站的文章部分内容可能来源于网络，仅供大家学习与参考，如有侵权，请联系站长QQ：805375623进行删除处理。
4、本站一切资源不代表本站立场，并不代表本站赞同其观点和对其真实性负责。
5、本站一律禁止以任何方式发布或转载任何违法的相关信息，访客发现请向站长举报
6、本站资源大多存储在云盘，如发现链接失效，请联系我们我们会第一时间更新。

THE END
Python（EN）
# python# programming# computerscience# data
喜欢就支持一下吧
点赞15 分享
We are the tender love in the world.
我们都在被这个世界温柔的爱着
评论 抢沙发

请登录后发表评论

    暂无评论内容

文章目录
kity的头像-拾光赋
文章目录
今日剩余 89.8%
本周剩余 12.8%
本月剩余 36.3%
本年剩余 70.1%
最近评论
杀人蜂的头像-拾光赋

杀人蜂6天前0

我很好奇这个网站能提供什么
Vito的头像-拾光赋

Vito6个月前2

你就是我心中的那首忐忑，总是让我惊心动魄
Jason.Meng的头像-拾光赋钻石会员

Jason.Meng徽章-人气大使-拾光赋6个月前2

总是会在人生的不同阶段反复爱上大佬的文章~
每日一言
We are the tender love in the world.
我们都在被这个世界温柔的爱着