CHAPTER 01 Introduction to Pandas

1.1 Introduction to Pandas
1.1.1 What is the pandas library?
1.1.2 Pandas built into Microsoft Excel
1.1.3 Advantages of Pandas
1.2 Python Development Environment
1.2.1 Introducing Google Colab
1.2.2 How to Use Colab

CHAPTER 02 Python Basics and NumPy Library

2.1 Variables and data types
2.1.1 Variables
2.1.2 Integer
2.1.3 Mistakes
2.1.4 Strings
2.1.5 Fire
2.1.6 List
2.1.7 Tuples
2.1.8 Dictionary
2.2 Control statements and functions
2.2.1 Control statements
2.2.2 Function
2.3 Classes, Objects, and Libraries
2.3.1 Classes and Objects
2.3.2 Library
2.3.3 NumPy library

CHAPTER 03 Data Frames and Series

3.1 Data Frame
3.1.1 What is a data frame?
3.1.2 Checking the structure of the data frame
3.1.3 Creating a data frame
3.1.4 Parameters, Arguments, and Default Values
3.2 series
What is the 3.2.1 series?
3.2.2 Creating a Series
3.2.3 Checking the structure of the series
3.3 Loading a data frame from a file
3.3.1 Loading a data frame from an Excel file (read_excel)
3.3.2 Loading a data frame from a CSV file (read_csv)
3.4 Saving a data frame
3.4.1 Converting to a dictionary (to_dict)
3.4.2 Save to file (to_excel, to_csv)
3.4.3 Functions, methods, and properties
3.5 Exploring Data Frames
3.5.1 Understanding simple information about a data frame (info, etc.)
3.5.2 Retrieving only part of a data frame (head, tail)
3.5.3 Checking descriptive statistics of a data frame (describe)
3.5.4 Checking for unique values ​​in each column (unique, nunique)
3.5.5 Finding the frequency of unique values ​​(value_counts)
3.5.6 Drawing a histogram (hist)
3.5.7 Visualizing Data Frames and Series (plot)

CHAPTER 04 Index

4.1 Indexing and Slicing
4.1.1 Bracket Indexing
4.1.2 Creating columns using bracket indexing
4.1.3 Modifying columns with bracket indexing
4.1.4 Bracket Slicing
4.1.5 Key Indexing and Slicing (loc Indexer)
4.1.6 Creating rows and columns with the loc indexer
4.1.7 Location Indexing and Slicing (iloc Indexer)
4.1.8 Indexing and Slicing Summary
4.2 Functions for extracting data
4.2.1 Deleting rows and columns
4.2.2 Filtering by column name
4.2.3 Selecting columns by data type (select_dtypes)
4.2.4 Modifying Pandas Functions and Sources
Excel Example 1: Indexing and Slicing Titanic Sinking Passenger Data
4.3 Functions that handle indices and columns
4.3.1 Setting the index (set_index)
4.3.2 Resetting the index (reset_index)
4.3.3 Changing Indexes and Columns (Assignment)
4.3.4 Changing indexes and columns (set_axis)
4.3.5 Renaming a column
4.3.6 Mapping and Mappers
4.3.7 Reindexing Data
4.3.8 Changing the index name (rename_axis)
4.3.9 Converting an index class to a list (tolist)
4.4 Introduction to Multi-Index
4.4.1 Multi-Index and Level
4.4.2 Changing the structure of a multi-index (stack, unstack)

CHAPTER 05 Operations

5.1 Introduction to Pandas Operations
5.1.1 Vectorized Operations
5.1.2 Operators and Operation Functions
5.2 Series Operations
5.2.1 Operations on Series and Single Values
5.2.2 Operations between series
5.2.3 Operations between columns in a data frame
5.3 Operations on data frames
5.3.1 Operations on Data Frames and Single Values
5.3.2 Operations between data frames
5.3.3 Performing calculations by replacing NaN (fill_value of calculation functions)
5.3.4 Operations on Data Frames and Series (Broadcasting)
Excel Example 2: Daily Stock Price Trend Analysis
5.4 Applying statistical functions
5.4.1 Aggregate functions
5.4.2 Cumulative statistics functions
5.4.3 Parameter axis and axis specification
5.4.4 Other statistical functions
Excel Example 3: Practicing Various Statistical Functions

CHAPTER 06 Data Cleaning

6.1 Introduction to Data Cleaning
6.1.1 Introduction to the Data Analysis Process
6.1.2 What is data cleansing?
6.2 Sorting
6.2.1 Sorting by a single column (sort_values)
6.2.2 Ascending and descending order
6.2.3 Sorting by multiple columns
6.2.4 Sorting by index or columns (sort_index)
6.3 Filtering
6.3.1 What is Boolean Indexing?
6.3.2 Single Requirement Boolean Indexing
6.3.3 Multi-Requirement Boolean Indexing
6.3.4 Functions that create Boolean data type objects
6.3.5 Retrieving only a portion of data based on the values ​​of a specific column (nlargest, nsmallest)
6.3.6 Randomly extracting data (sample)
Excel Example 4: Extracting Desired Data from OECD Country GDP Data
6.4 Handling Missing Values ​​1
6.4.1 Checking for missing values ​​(isna)
6.4.2 Deleting data containing missing values ​​(dropna)
6.4.3 Imputing missing values ​​(fillna)
6.5 Handling Outliers and Duplicate Data
6.5.1 Handling Outliers (clip)
6.5.2 Check for and remove duplicate data (duplicated, drop_duplicates)
6.5.3 Using the duplicate data processing function
6.6 Data type conversion and decimal point handling
6.6.1 Converting to various data types (astype)
6.6.2 Converting to numeric type (to_numeric)
6.6.3 Handling decimal points
6.7 Substitution and Mapping
6.7.1 Replacing Data
6.7.2 Mapping Data (map)
6.7.3 Difference between replace and map functions
Excel Example 5: Cleaning Tip Data from U.S. Restaurant Customers

CHAPTER 07 Combining Data

7.1 Concatenating Data Frames
7.1.1 Concatenating Data Frames (concat)
7.1.2 Outer and Inner Joins
7.2 Merging Data Frames
7.2.1 Merge using Excel's VLOOKUP method
7.2.2 Performing a multi-requirement vlookup with the merge function
7.2.3 Merge method of the merge function
Excel Example 6: Tallying the Major League Baseball batters with the highest percentage of home runs among team home runs by season.
7.3 Update
7.3.1 Updating a Data Frame
7.3.2 Updating a data frame (combine_first)
7.4 Merge into ranges
7.4.1 Merge by range (merge_asof)
7.4.2 Splitting groups and merging them into ranges
Excel Example 7: Calculating Sales Amount Using Print Shop Sales Data

CHAPTER 08 Heat Processing

8.1 Heat Processing
8.1.1 What is heat processing?
8.1.2 Introduction to Various Thermal Processing
8.2 Processing heat with various operations
8.2.1 Processing columns with object-to-object operations
8.2.2 Processing columns with operations within objects
8.2.3 Processing heat with mathematical operations
8.3 Ranking
8.3.1 Ranking
8.3.2 Various tie-breaking methods for the rank function
8.4 Boolean Masking
8.4.1 Boolean Masking with Boolean Indexing
8.4.2 Masking Booleans with Pandas Functions (mask, where)
8.4.3 Boolean Masking with NumPy's np.where Function
8.4.4 Boolean Masking with NumPy's np.select Function
8.5 Categorization of numeric data
8.5.1 Cut into categories by dividing the intervals by numbers
8.5.2 Divide intervals by percentile and categorize (qcut)
Excel Example 8: Processing Columns with Students' Height and Weight Data (1)
8.6 Handling Missing Values ​​2
8.6.1 Replacing missing values ​​with forward and backward data (ffill, bfill)
8.6.2 Interpolating missing values
8.7 Processing columns with row-to-row operations
8.7.1 Moving Data (Shift)
8.7.2 Finding the difference between rows (diff)
8.7.3 Finding the rate of change between rows (pct_change)
Excel Example 9: Samsung Electronics Stock Price Analysis

CHAPTER 09 apply

9.1 Introducing the apply function
9.1.1 Why the apply function is needed
9.1.2 Function of the apply function
Applying the apply function to the 9.2 series
Applying the apply function to the 9.2.1 series
9.2.2 User-defined functions and apply
9.2.3 lambda functions and apply
9.2.4 Things to keep in mind when applying the apply function to the series
9.2.5 Additional Learning on Lambda Functions
Excel Example 10: Processing Columns with Students' Height and Weight Data (2)
9.3 Applying the apply function to a data frame
9.3.1 Applying the apply function to a data frame
9.3.2 Specifying the axis when applying the apply function to a data frame
9.3.3 Applying a lambda function to each row of data input from multiple columns
9.3.4 Comparison of apply and map functions
Excel Example 11: Preprocessing and Analyzing Subway Station Data

CHAPTER 10 HANDLING STRINGS

10.1 Functions for handling strings
10.1.1 Why Learn Pandas String Handling Functions?
10.1.2 Features of Pandas string handling functions
10.2 Various functions for handling strings
10.2.1 Indexing and Slicing
10.2.2 Returning the length of a string (str.len)
10.2.3 Removing spaces from a string (str.strip and others)
10.2.4 Splitting a string (str.split)
10.2.5 String replacement (str.replace and others)
Excel Example 12: Converting GDP-Related Data to Numeric Format
10.2.6 Checking if a string contains a value (other than str.contains)
10.2.7 Extracting Strings (str.extract)
10.3 Regular Expressions
10.3.1 What are regular expressions?
10.3.2 Main Grammar of Regular Expressions
10.3.3 Using Regular Expressions with Pandas String Functions
10.3.4 Extracting Strings Using Regular Expressions (str.extractall, etc.)
Excel Example 13: Counting the Number of Coffee Franchise Stores in Seocho-gu and Gangnam-gu

CHAPTER 11 Pivots and Unpivots

11.1 Pivot Table
11.1.1 Why Use Pivot Tables?
11.1.2 Pivot Tables and Aggregate Functions
11.1.3 Creating a Pivot Table (pivot_table)
11.1.4 Creating a Pivot Table by Entering Multiple Arguments
11.1.5 How to input a function as an argument
11.1.6 Aggregate functions that apply only to grouping (first, last)
11.1.7 String Pivot
11.1.8 Creating a crosstab that aggregates frequencies (crosstab)
Excel Example 14: In the Titanic sinking, were women and children rescued first?
11.2 Unpivot
11.2.1 Why Unpivot is Needed
11.2.2 Unpivoting with the stack function
11.2.3 Unpivoting with the melt function
11.2.4 Differences in unpivoting between stack and melt functions
Excel Example 15: Creating Various New Pivot Tables Using Sales Data from a Supermarket

CHAPTER 12 Grouping Data

12.1 Processing columns with the groupby function
12.1.1 Why the groupby function is needed
12.1.2 Applying functions within groups (groupby)
12.1.3 Ranking with the groupby function
Excel Example 16: Distinguishing Between People with the Same Name
12.1.4 Row-by operation with groupby function (shift and other)
12.1.5 Replacing missing values ​​with forward and backward values ​​using the groupby function (ffill, bfill functions)
12.1.6 Calculating cumulative sum using groupby function (cumsum)
12.1.7 Finding the order using the groupby function (cumcount)
Excel Example 17: Processing Various Columns Using the GroupBy Function with Stock OHLCV Data
12.1.8 Transforming aggregate results into columns
12.1.9 Applying a User-Defined Function with the Transform Function
Excel Example 18: Divide into groups, calculate standard scores, and assign grades to students.
12.2 Aggregating with the groupby function
12.2.1 Applying Aggregation Functions with the Groupby Function
12.2.2 Differences between aggregating with the groupby function and pivot tables
12.2.3 Aggregating with groupby and agg functions
12.2.4 Joining strings
12.2.5 Applying User-Defined Functions with the agg Function
12.2.6 Difference between agg and transform functions
Excel Example 19: Creating Various Aggregations Using Titanic Sinking Passenger Statistics
12.3 Advanced groupby
12.3.1 Various parameters of the groupby function
12.3.2 Group By Objects
12.3.3 groupby function and head, tail, and sample
12.3.4 Grouper
12.3.5 Various group filtering (all, any) using the transform function
Excel Example 20: Aggregating Used Audi Car Prices

CHAPTER 13 TIME SERIES DATA

13.1 Introduction to Time Series Data
13.1.1 Types of time series data
13.1.2 datetime data type
13.1.3 Learning Focus on Time Series Data
13.2 Converting and Indexing Time Series Data
13.2.1 Converting to time series data (to_datetime)
13.2.2 Other time series transformation functions
13.2.3 Loading a data frame by specifying the datetime data type from a file
13.2.4 Indexing and Slicing DatetimeIndex
13.2.5 Extracting data for a specific time period (at_time, between_time)
Excel Example 21: Handling Online Shopping Mall Data with Time Series Data (1)
13.3 Time series data generation and cycle
13.3.1 Cycle
13.3.2 Creating time series data (date_range)
13.4 Grouping Time Series Data
13.4.1 Processing columns by grouping (resample)
13.4.2 Comparison of the resample and groupby functions
13.4.3 Aggregating groups (resample)
13.4.4 Applying the agg function to the resample function
13.4.5 Applying the groupby function and the resample function simultaneously
Excel Example 22: Handling Online Shopping Mall Data with Time Series Data (2)
13.5 Extracting Specific Time Series Data
13.5.1 Various methods for extracting specific time series data (dt accessor)
13.5.2 Converting to a string (strftime)
13.5.3 Converting to period data type (to_period)
Excel Example 23: Handling Online Shopping Mall Data with Time Series Data (3)
13.6 Other functions for handling time series data
13.6.1 Time Zone Conversion
13.6.2 Creating a Time Interval (DateOffset)
13.6.3 Creating a Time Series Grouper (Grouper)
13.6.4 Creating an array of only business days (bdate_range)
13.6.5 Upsampling (asfreq and others)
Excel Example 24: Upsampling Bitcoin Purchase Logs

CHAPTER 14: Advanced Pandas 1

14.1 Useful Pandas Functions to Know
14.1.1 Changing column names in bulk (add_prefix, add_suffix)
14.1.2 Returning data from a column and then deleting it (pop)
14.1.3 Creating a column at a specific location (insert)
14.1.4 Creating a column (assign)
14.1.5 Filtering with a query
14.1.6 Returning the location of a row or column (get_loc)
14.1.7 Converting an index class to a data frame or series (to_frame, to_series)
14.1.8 Joining Data Frames
14.1.9 Using consecutive methods (pipe)
14.1.10 Encoding categorical data (factorize)
14.1.11 Performing one-hot encoding (get_dummies)
14.1.12 Performing one-hot encoding of a string series (str.get_dummies)
14.1.13 Explode a list of cells into rows
14.1.14 Encoding based on multiple columns (ngroup)
14.1.15 Checking if data frames or series are exactly the same (equals)
14.1.16 Returning different parts of two objects (compare)
14.1.17 Converting a horizontal data frame to a vertical one (wide_to_long)
14.1.18 Loading a data frame from an HTML table (read_html)
14.2 Useful NumPy functions to know
14.2.1 NumPy's random number generation function
14.2.2 Returning the larger value of two arrays (np.fmax)
14.2.3 Sorting each row or column individually (np.sort)
14.3 Index Class
14.3.1 Creating an Index Class
14.3.2 Functions applied to the index class
14.4 Multi-Index
14.4.1 Creating a multi-index
14.4.2 Indexing a DataFrame with Multiple Indices
14.4.3 Functions that handle multiple indices
14.4.4 Interval Index

CHAPTER 15: Advanced Pandas 2

15.1 Moving and Cumulative Aggregations
15.1.1 Rolling
15.1.2 Expanding
15.1.3 Deepening Moving and Cumulative Aggregations
15.2 Category data types
15.2.1 Why Use Category Data Types?
15.2.2 Converting to category data type
15.2.3 Various methods of category data types
15.3 Visualization
15.3.1 Color
15.3.2 Parameters of the plot function
15.3.3 Splitting a region to create a subgraph
15.4 Pandas Tips
15.4.1 Scientific Notation
15.4.2 Separate digits with commas
15.4.3 Expressing decimals as percentages
15.4.4 Merge all sheets in an Excel file at once
15.4.5 Splitting a data frame into groups and saving it as an Excel file
15.4.6 Entering a lambda function into Boolean indexing
15.4.7 Changing column names by applying a lambda function to the rename function
15.4.8 Sorting by applying a function to the sorting criteria (parameter key)
15.4.9 Swapping positions in a string using a capture group in a regular expression
15.4.10 Creating Regular Expression Patterns with Python's join Function
15.4.11 Reverse a series or data frame
15.4.12 Determining the data type of individual elements within an object column
15.4.13 Consolidating low-weight categories
15.4.14 Converting multiple indices to a single index using the map function
15.4.15 Filtering based on a specific point in time using cummin and cummax functions
15.4.16 Returning the first non-NaN value
15.4.17 Expanding lists and dictionaries within a cell into separate columns
15.4.18 Aggregating only columns with numeric data
15.4.19 Create a row that aggregates subtotals for each group
15.4.20 Grouping multiple columns by setting them to the same group regardless of order
15.4.21 Converting date data in Excel to datetime when importing it as a number
Find the difference from the last date of the period 15.4.22
15.4.23 Moving data in time series cycles
15.4.24 Categorizing by interval index
15.4.25 Applying the apply function using data from the index

CHAPTER 16: Practical Data Analysis

16.1 Bollinger Bands and Stock Price Trend Analysis
16.2 Moneyball and Baseball Data Analysis
16.3 Analysis of National Football Team A Match Results
16.4 Franchise Distance Analysis
16.5 Big Data and Securities Balance Analysis

Do you work with data but feel limited by Excel alone?
Want to get started with data analysis?
Learn Pandas with "Python's Excel, the Pandas Library"!

This book consists of a total of 16 chapters.
Chapters 1-5 are the 'preliminary learning' required to handle data with Pandas in earnest.
Starting with the basic Python for learning Pandas, you will learn core concepts and learn how to handle Pandas' core classes: dataframes, series, and indices.
Next, we will cover the simple yet powerful function of Pandas called 'operations'.

Chapters 6-13 cover data analysis in earnest.
You will systematically learn the process of data analysis, which broadly consists of 'data cleaning', 'data combination', 'column processing', and 'grouping'.
This is the content you need to jump from beginner to intermediate level, and it is also very helpful for intermediate and practitioners.

Chapters 14 and 15 cover useful functions based on the main framework of Pandas learned so far, as well as in-depth information on indexes, moving and cumulative aggregate functions, categorical data types, and visualization.
You'll also learn Pandas tips to improve the efficiency of your data analysis.

Finally, in Chapter 16, you will gain a feel for data analysis by experiencing practical data analysis on various topics such as stock prices, baseball, soccer, franchises, and securities firms.

Features of this book

- We provide detailed and friendly explanations based on abundant pictorial materials.
- You can solidify what you have learned through various Excel examples and practical data analysis problems.
- It is organized step by step, from beginner level to content that will be helpful to practitioners.
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