**Complete Data Analysis Course with Pandas & NumPy : Python**

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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