English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 6 Hours | 2.59 GB
This Python course will get you up and running with using Python for data analysis and visualization.
Pandas is an open-source, BSD-licensed Python library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. This Python course will get you up and running with using Python for data analysis and visualization.
The training will include the following;
- Installing Jupyter
- Jupyter Environment
- Read data using Pandas
- Series vs Data Frame
- Basic Operations in Pandas
- Analyze the imported data
- Renaming Columns
- Sorting
- Filtering Data
- Filtering Function
- Read Selective Columns & Rows
- And a lot other topics.
Table of Contents
Introduction
1 Introduction to Pandas with Python
Data Set
2 Understanding Jupiter Environment
3 Reading the Data Set
4 Series and Data Frame
5 Operations in Data Set
6 More on Panda Functions
7 Column Names and Operation
8 Removing Columns and Rows
9 Sorting Data Frame
10 Filtering Data
Data Analysis
11 Filter Multiple Criteria
12 Plotting series in Pandas
13 Dealing with Null Values
14 Uses of Index
15 Column in Index
16 Output of Data
17 Functions of iX Method
18 InPlace Parameter
19 Inspecting the Space
20 Reducing the Space
21 Using in Country Series
22 Selective Columns and Rows
23 Creating Manual Data Frame
24 Random Sampling with Pandas
25 Concept of Dummy Coding
26 Creating Dummified Values
27 Duplicates in Data Frame
28 Functions for Date and Time
29 Setting with Copy Warning
30 Example on Copy Warning
31 Changing the Display Option
32 Formatting the Data
33 Data Frame and Series
34 Tricks for Display Options
35 Data with Rows and Columns
36 Converting Data Frame
37 Axis Parameter
38 String Methods in Pandas
39 Changing the Data Types
40 Example of Data Type Change
41 Group by Functions
42 Functions on Series
Azure Data Lake
43 Introduction to Azure Data Lake
44 More on Crosstab
45 More Options with Crosstab
46 Functions of Pivot
47 Pivot Table Method
48 Example on Pivot Table
49 Data Frame to CSV File
50 Using Excel Functions
51 Merging Data Frames
52 Shaping a Data Frame
53 Filling NA Values
54 Importing Time Series Data
55 Working with Interpolate Method
56 Stacking and Unstacking
57 Stacking and Unstacking for 3 Levels
58 Concept of Crosstab
Summary
59 Summary on Pandas
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