Python and Pandas Quick Reference Tutorials
4.5 (1 rating)
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Python and Pandas Quick Reference Tutorials

For beginners and intermediate Python/Pandas users
4.5 (1 rating)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
35 students enrolled
Created by Henry Palma
Last updated 5/2020
English
English [Auto]
Current price: $13.99 Original price: $19.99 Discount: 30% off
5 hours left at this price!
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This course includes
  • 5.5 hours on-demand video
  • 38 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Python
  • Pandas
  • Data Analysis
  • Jupyter Notebook
  • Data Science
Course content
Expand all 51 lectures 05:15:54
+ Pandas Tutorials
19 lectures 02:24:00

This video contains an introduction to the entire series. I introduce myself and the sequence of videos.

Preview 01:20

We will be using the Jupyter notebook throughout this series. This video guides you on installing the jupyter notebook.

Preview 03:14

This video shows you how to launch the Jupyter Notebook from the Anaconda Launcher and from command line.

Pre-Requisite: Using the Jupyter Notebook
05:59

This section covers the 2 main data structures in the Pandas Library, the Series and the DataFrame.

Preview 05:55

This video covers opening a local csv file into a DataFrame and doing some basic attribute analysis.

  • Using read_csv to load a local csv file as a DataFrame

  • Getting the column and row counts of a DataFrame using the shape attribute

  • Getting the column datatypes using the dtypes attribute

Preview 05:25

This video covers selecting both columns and rows from a DataFrame.

  • Selecting a single column from a DataFrame

  • Selecting multiple columns from a DataFrame

  • Selecting rows based on a text value in a DataFrame

  • Selecting rows based on a numeric value in a DataFrame

Data Analysis QuickStart: Viewing and Selecting DataFrame Data
05:55

This video covers adding both static and computed values to a DataFrame.

  • Add a Static column to a DataFrame

  • Add a Computed column to a DataFrame

  • Add a Column to a DataFrame using a Lambda Function

  • Add a Column to a DataFrame using a Function

  • Export DataFrame to a new csv

Data Analysis QuickStart: Adding Columns to a Pandas DataFrame
10:12

This video covers data aggregation techniques using GroupBy and PivotTable.

  • Calculate Sales by Associate using Group By

  • Calculate Sales by Product using Group by

  • Calculate Revenue by Product using Pivot_Table

  • Flatten a Pivot using melt()

Data Analysis: Aggregating Data using Groupby and Pivot a Pandas DataFrame
07:42

This video covers plotting data from a DataFrame.

  • Prepare data for Plotting

  • Create a pie Graph

  • Create a bar Graph

Preview 04:14

This tutorial covers the many ways to create a DataFrame.

- Create an Empty DataFrame

- Create a DataFrame with Random Values using numpy random randint()

- Create a DataFrame from a Python Dictionary

- Create a DataFrame from a Python List

- Create a DataFrame from a local CSV file

- Create a DataFrame from an online CSV file

- Create a DataFrame from a local JSON file

- Create a DataFrame from an online JSON file

8 Techniques to Create a DataFrame
12:37

This section covers the many ways to filter/search a DataFrame.

  • Filter DataFrame by column name

  • Filter DataFrame rows by column values:equals a value

  • Filter DataFrame rows by column values:doesn’t equal a value

  • Filter DataFrame rows by column values is in a list of values

  • Filter DataFrame rows by column values is not in a list of values

  • Filter DataFrame rows by column values is null

  • Filter DataFrame rows by column values is not null

  • Filter DataFrame rows using query

  • Filter DataFrame rows using iloc

  • Filter DataFrame rows using loc

10 Techniques to Filter/Search a DataFrame
12:04

This tutorial will cover 12 different techniques to update a Pandas DataFrame

  1. Add rows to a DataFrame using append()

  2. Remove items from a DataFrame using drop()

  3. Replace a value in a DataFrame using replace()

  4. Add a single value to a new column of a DataFrame

  5. Add a mean(),max(),median() column to a DataFrame

  6. Update a column of a dataframe with applymap

  7. Add a column to a dataframe using applymap

  8. Add a column to a dataframe using map

  9. Update a column to a dataframe with map

  10. Add a column to a dataframe with apply

  11. Fill null values using fillna()

  12. Update the datatype of a dataframe using astype()

12 Techniques to Update A DataFrame
16:25

This video coves the applymap() function on all columns and a subset of columns of a DataFrame.

DataFrame Technique (ApplyMap) on a DataFrame
04:12

This tutorial covers applying the map function to Pandas DataFrame to create a new column or update an existing column.

DataFrame Technique (Map) function on a DataFrame
03:49

Techniques to Export a DataFrame

  1. Export a Dataframe to CSV

  2. Export a Dataframe to Excel

  3. Export a Dataframe to a Python Dictionary

  4. Export a Dataframe to a json file

  5. Export a Dataframe to a pickle file

  6. Export a Dataframe to the clipboard for copy/paste

6 Techniques to Export a DataFrame
04:24

This lecture will cover 8 techniques to create a Pandas Series

  1. Create an Empty Series

  2. Create a Series with Random Values

  3. Create a Series from a Python Dictionary

  4. Create a Series from a Python List

  5. Create a Series from an local CSV file

  6. Create a Series from an online CSV file

  7. Create a Series from a Local JSON file

  8. Create a Series from a JSON file

8 Techniques to Create a Pandas Series
16:29

8 Techniques to Analyze a Series

1. Get the name of a Series using name attribute

2. Get the row count of a Series using size attribute

3. Get the datatype of a Series using dtype attribute

4. Get all the Statistics of a Series using describe()

5. Get the MEAN of a Series using mean()

6. Return the first few rows of a Series using head()

7. Sort values in a Series using sort_values()

8. Sort indexes in a Series usisng sort_index()

Pandas Series Attributes for Analysis
04:53

9 Techniques to Search/Filter a Series.

1. Get the name of a Series using name attribute

2. Get the row count of a Series using size attribute

3. Get the datatype of a Series using dtype attribute

4. Get all the Statistics of a Series using describe()

5. Get the MEAN of a Series using mean()

6. Return the first few rows of a Series using head()

7. Sort values in a Series using sort_values()

8. Sort indexes in a Series usisng sort_index()

9 Techniques to Select and Filter a Series
10:55

5 Techniques to Update a Series

  1. Add items to a Series using append()

  2. Replace items in a Series using replace()

  3. Remove items of a Series using drop()

  4. Fill null values using fillna()

  5. Update a value in a Series by index and update()

Preview 08:16
+ (Optional) Python Fundamentals
20 lectures 02:37:57

This video covers the "Hello World" program as well as single line and multi-line comments

Hello World in Python
04:15

This video reviews the reserved keywords in Python.

Keywords in Python
02:01

This video covers the 4 main datatype in Python (int, float, boolean, string). It shows you how to create variables of different types and also how to caste from one datatype to another.

Python Variables and DataTypes
06:07

This video covers performing math calculations in Python.

Preview 02:52

This video covers creating, analyzing and updating strings in Python.

Strings in Python
13:06

This video covers creating, analyzing and updating strings in Python.

String.format() in Python
04:01

This video covers some of the Python Standard Lib Modules (Math, Random, Re).

Standard Lib in Python
15:52

This video covers getting user input in Python.

Getting User input in Python
02:19

This video covers if,elif,else statements used for decision making in Python.

Decision Making in Python
12:52

This video covers creating loops in Python. This includes for loops and while loops.

Preview 07:45

This video covers creating and working with Lists in Python.

Lists in Python
11:17

This video covers creating Dictionaries in Python.

Dictionaries in Python
05:51

This video covers creating and working with Sets in Python.

Preview 09:54

This video covers creating and working with Tuples in Python.

Tuples in Python
06:56

This video covers creating and working with List Comprehensions in Python.

List Comprehensions in Python
05:27

This video covers working with and using dates and datetime objects in Python.

Dealing with Dates and Time in Python
11:08

This video covers creating and using functions in Python.

Functions in Python
07:38

This video covers creating and using lambda functions in Python.

Preview 03:39

This video covers creating and using classes in Python.

Classes in Python
10:20

This video covers creating exceptions in Python.

Exceptions in Python
14:37
+ Mark Down Tutorials (Optional)
12 lectures 13:57

Covers creating Headers in Markdown.

MarkDown Creating Headers
00:40

Covers creating Horizontal lines in Markdown.

MarkDown Creating Horizontal Lines
00:28

Covers creating bulleted lists in Markdown.

Preview 00:25

Covers creating numbered lists in Markdown.

MarkDown Numbered Lists
00:25

Covers creating Nested Lists in Markdown.

MarkDown Nested Lists
00:51

Covers indented text in Markdown.

MarkDown Indented Text
00:43

Covers creating formatted text in Markdown.

MarkDown Formatted Text
01:05

Covers creating links in Markdown.

MarkDown Links
00:37

Covers adding images to Markdown.

MarkDown Images
01:52

Covers creating tables in Markdown.

MarkDown Tables
02:36

Covers creating latex formulas and using Greeks in Markdown.

MarkDown Latex and Greeks
01:28

Covers the various keyboard shortcuts that exist in the Jupyter Notebook.

Keyboard ShortCuts
02:47
Requirements
  • Have analyzed data in excel
  • Not afraid of a little code.
Description

This program is designed for professionals curious about data analysis and data engineering with Python and the Pandas Data Analysis Library. You don't need to be an experienced programmer to learn in these tutorials. I will give you some code and explain how it works. You will be able to take that code and use it in your day to day learning. In my experience learning to program really boils down to learning some code and then tinkering until you understand how things work. This program is designed that way.

Who this course is for:
  • Busy professionals looking to advance their data analysis skills and learn some data science and programming.