Data Analysis And Visualization Using Python

The student will gain knowledge of Python libraries pandas and matplotlib and data analysis and vizualization

What you'll learn

Basics in pandas library

File reading and writing

Data visualization using matplotlib

Data wrangling

Data agreggation

Time series

Requirements

The student should have basic understanding of Python programming language

Description

The course title is “Data analysis and visualization using Python” and it is using the pandas library.It is divided into 7 chapters.Chapter 1 talk about creation of pandas objects such as: Series, DataFrame, Index. This chapter includes basic arithmetic with pandas object. Also it describes other operations with pandas object such as: reindexing, deleting data from axis, filtering, indexing and sorting.Chapter 2 describes statistical methods applied in pandas objects and manipulation with data inside pandas object. It describes pandas operations such as: unique values, value counting, manipulation with missing data, filtering and filling missing data.Chapter 3 talks about reading and writing data from text file format and Microsoft Excel. Partial reading of large text files is also described with an example.Chapter 4 describes data visualization using matplotlib library. It has example about the following graphs: line, scatter, bar and pie. Setting title, legend and labels in the graph is also describes with some practical examples. Drawing from pandas object is also described.Chapter 5 talks about data wrangling. Merging Series object and DataFrame object is described with practical examples. Combining pandas objects and merging them is part of this chapter.Chapter 6 talks about various forms of data aggregation and grouping. Creating and using pivot tables is also described.Chapter 7 talks about time Series creation and manipulation. Classes DatetimeIndex and Period are included in the description of the chapter. Indexing and selection is described with practical examples.

Overview

Section 1: Introduction to pandas library

Lecture 1 Series part 1

Lecture 2 Series Part 2

Lecture 3 DataFrame part 1

Lecture 4 DataFrame part 2

Lecture 5 Index object

Lecture 6 Reindexing

Lecture 7 Deleting data from the axis

Lecture 8 Indexing, selection and filtering Part 1

Lecture 9 Indexing, selection and filtering Part 2

Lecture 10 Indexing, selection and filtering Part 3

Lecture 11 Arithmetics with Series and DataFrames

Lecture 12 Functions and mapping

Lecture 13 Sorting in pandas

Lecture 14 Indexes with duplicate values

Lecture 15 Class work no 1

Lecture 16 Solution to class work no 1

Section 2: Operations in pandas library

Lecture 17 Statistical description methods in pandas part 1

Lecture 18 Statistical description methods in pandas part 2

Lecture 19 Unique Values and Value counting part 1

Lecture 20 Unique Values and Value counting part 2

Lecture 21 Manipulation of missing data

Lecture 22 Filtering missing data

Lecture 23 Filling missing data

Lecture 24 Hierachical indexing Part 1

Lecture 25 Hierachical indexing Part 2

Lecture 26 Using dataframe columns

Lecture 27 Class work no 2

Lecture 28 Solution to class work no 2

Section 3: Reading and writing data to the file

Lecture 29 Reading and writing data from file Part 1

Lecture 30 Reading and writing data from file Part 2

Lecture 31 Partial reading of text files

Lecture 32 Writing data out to text format

Lecture 33 Reading Excel files

Lecture 34 json data

Lecture 35 Class work no 3

Lecture 36 Solution to class work no 3

Section 4: Data visualization

Lecture 37 Data vizualization Line drawing

Lecture 38 Scatter graph

Lecture 39 Bar graph

Lecture 40 Pie graph

Lecture 41 Advanced drawing 2d

Lecture 42 Title tick and label positioning

Lecture 43 Legend positioning

Lecture 44 Line drawing in pandas

Lecture 45 Bar drawing in pandas

Lecture 46 Scatter graph in pandas

Lecture 47 Class work no 4

Lecture 48 Solution to class work no 4

Section 5: Data wrangling in pandas

Lecture 49 Data wrangling

Lecture 50 Merging DataFrames part 1

Lecture 51 Merging DataFrames part 2

Lecture 52 Merging index objects

Lecture 53 Concatenation in pandas Part 1

Lecture 54 Concatenation in pandas Part 2

Lecture 55 DataFrame Rearrangement

Lecture 56 Removing dupliate data

Lecture 57 Data transformation using function or mapping

Lecture 58 Replacing values in pandas

Lecture 59 Renaming indexes in pandas

Lecture 60 Class work no 5

Lecture 61 Solution to class work no 5

Section 6: Data grouping and aggregation

Lecture 62 Groupby mechanics part 1

Lecture 63 Groupby mechanics part 2

Lecture 64 Group iteration

Lecture 65 Column selection

Lecture 66 Grouping with dictionary and Series

Lecture 67 Grouping with functions

Lecture 68 Data aggregation

Lecture 69 Grouping by columns

Lecture 70 Multiple functions application

Lecture 71 General form of operation split apply combine

Lecture 72 Pivot tables

Lecture 73 Class work no 6

Lecture 74 Solution to class work no 6

Section 7: Time series

Lecture 75 Time series introduction

Lecture 76 Date and time data types

Lecture 77 Converting from string to date

Lecture 78 Time series basics

Lecture 79 Indexing and selection

Lecture 80 Time series with double indexes

Lecture 81 Resample conversion

Lecture 82 Date range generation

Lecture 83 Frequencies and date shift

Lecture 84 Data replacement (before and after)

Lecture 85 Periods and arithemtics

Lecture 86 Period conversion

Lecture 87 Conversion from timestamps to periods

Lecture 88 Creation of PeriodIndex from arrays

Lecture 89 Resampling and frequency conversion Downsampling

Lecture 90 Upsampling

Lecture 91 Drawing time series

Lecture 92 Drawing time series example

Aspiring data analyst,Data analyst,Students that want to have knowledge about pandas library