English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 11.5 Hours | 4.20 GB
Learn most in demand skill in space of Data Science, Data analytics : Data analysis library Pandas & NumPy – Python
Update : New section on Numpy Library get added.
There era of Microsoft Excel is going to be over, so would you like to learn the next generation one of the most powerful data processing tool and in demand skill required for data analyst, data scientist and data engineer.
Then this course is for you, welcome to the course on data analysis with python’s most powerful data processing library Pandas.
Why this course?
Data scientist is one of the hottest skill of 21st century and many organisation are switching their project from Excel to Pandas the advanced Data analysis tool .
This course is basically design to get you started with Pandas library at beginner level, covering majority of important concepts of data processing data analysis and a Pandas library and make you feel confident about data processing task with Pandas at advanced level.
What is this course?
This course covers
- Basics of Pandas library
- Python crash course for any of you want refresh basic concept of python
- Python anaconda and Pandas installation
- Detail understanding about two important data structure available in a Pandas library
- Series data type
- Data frame data type
- How you can group the data for better analysis
- How to use Pandas for text processing
- How to visualize the data with Pandas inbuilt visualization tool
- Multilevel index in Pandas.
- Numerical Python : NumPy Library
What you’ll learn
- Update your resume with one of the in demand skill : Data analysis Pandas
- Setting up Python in anaconda environment
- Refresh Python basics with crash course
- Learn Most demanded python data analysis library : Pandas
- Three important data structure of pandas : Series, Data Frame, Panel
- Learn how to analyse one, two and three dimensional data
- How to group Data for analysis
- How to deal with Text Data with Pandas Functions
- Analyse data having multiple level index.
- Array and Matrix manipulation Library NumPy
Table of Contents
Introduction
1 What is Data analysis
2 Introduction to Pandas
3 Course FAQ
Installation and IDE
4 Different ways of installation
5 Download and Install anaconda + Pandas
6 Troubleshooting : ‘conda’ is not recognized as an internal or external command
7 Anaconda + Conda Command
8 Conda Cheatsheet
9 anaconda, conda & pandas Update
10 Getting started with Jupyter Lab
11 Jupyter Notebook cheatsheet
12 Import Library
Code Download
13 Python Code
Python Crash Course [Optional]
14 Introduction
15 Python Basics – I
16 Python Basics – II
17 Lists and tuples
18 Dictionary and set
19 Functions
Python Exercises
20 Exercise Overview
21 Solutions
Numpy
22 Creating NumPy array
23 Numpy indexing and selection, Functions
24 Some more Numpy Functions
25 Linear algebra with NumPy
26 List vs NumPy Array
27 Views vs Copy – Numpy Array
28 Insert, Append and Delete NumPy array
29 Split, Concatenate, Tile and Repeat array
Series : Pandas
30 Series
31 Introduction to Series
32 Create Series from Python Object
33 Create Series from CSV file
34 Series attributes & methods
35 Label indexing
36 inplace parameter, sort_values & sort_index
37 Apply Python built in function on Series
38 Extract Value from Series
39 .value_counts() Method
40 .apply() and .map() method
Data Frame : Pandas
41 Introduction to Data Frame
42 Create Data Frame – random data + from File
43 Data frame attributes and methods
44 Adding new column
45 Select one or more than one column
46 Broadcasting operation
47 Drop missing row or column
48 Filtering Data with one condition
49 Filtering Data with multiple condition
50 Filtering Data with .isin() method
51 Filtering Data with .between() method
52 unique() & nunique() method
53 sorting values
54 sort index and inplace parameter
55 .loc() and .iloc() method
56 .ix() method
57 .astype() method – optimize memory requirement
58 set_index() : change index column
59 .apply() method on single column
60 .apply() method on multiple column
61 Fetch random sample
Pandas Exercise
62 Exercise Overview : Google App store dataset
63 Pandas Exercise Solution – I
64 Pandas Exercise Solution – II
Panel : Pandas
65 Warning – Panel Data type
Pandas Options
66 max_rows , max_columns
67 precision
Visualize Data with Pandas
68 Display Stock data with Line Chart
69 Pie, Histogram and Bar Chart
Import and Export data from Pandas
70 read_csv() & to_csv() method
Working with Text Data
71 Getting started with Data
72 Some String methods
73 More String methods
74 Filtering Message with String
75 Splitting Text
76 Processing on Column names
Data Grouping
77 Importing Data : Grouping
78 Getting Group
79 Size, First and Last Method
80 Sum, Mean, Max, Min Method
81 .agg method
Data Frame : Multiindex
82 Import Data – Multiindex
83 Set multiple column as index
84 Sorting MultiIndex
85 Index – Meta Information
86 Change Index names
87 Fetch data from MultiIndex Dataframe
88 Transposing DataFrame
89 UnStack and Stack Data
90 Pivot and Pivot_table Method
Working with Time series data
91 Python Date and Datetime module
92 Pandas Timestamp and Datetimeindex object
93 Generate Time sequence
Data cleaning
94 Data cleaning – Youtube Dataset (warm up) Part – 1
95 Data cleaning – Youtube Channel Dataset Part – 2
96 Data cleaning – Youtube Channel Dataset Part – 3
Appendix : Numpy – Numerical Python Library
97 Notes
Bonus Lecture
98 Bonus Lecture
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