#19. Introduction to Exploratory Data Analysis (EDA) in Python | Tutorial

The video introduces Exploratory Data Analysis in Python.

Timeline & Data download link
(Python 3.7)

00:00 - Welcome
00:09 - Prerequisites
00:50 - Outline of video
02:14 - What is Exploratory Data Analysis?
04:28 - Open Jupyter Notebook
04:48 - Read .csv file
06:35 - About the data
06:54 - Pre-processing
07:33 - Type and description of data in columns using .dtypes()
10:26 - Find missing data and fill empty cells
12:33 - Visualize data
12:43 - Discussion on what columns could tell? df.iloc[0]
14:29 - Use the .unique() to get unique items from columns
15:13 - Discussion on how to use .min() and .max() to get ranges of values in columns?
19:37 - Discussion on how to filter a DataFrame to get a subset of interest
23:52 - Discussion on finding trends, create scatter plots
26:14 - Discussion on how to use .groupby() to get insight into the data
29:57 - Rename column after .reset_index()
30:26 - Discussion on how to use bar charts to get insight into the data
34:04 - Create multiple bar charts using subplots
38:40 - Discussion on what the bar charts inform about the data
39:40 - Discussion and filter DataFrame to narrow down the search for parameters further
47:19 - Mention about correlation and confounding factors
48:20 - Discussion and plot of histograms to see distribution of data in a column
54:26 - Ending notes


Link to download the file 'restaurant_data.csv'
https://github.com/learndataa/datasets

Thank you.