Practical Data Analysis with Pandas in Python | Exploratory Data Analysis & Data Wrangling / Munging
Practical, real world data analysis made possible by pandas!
Link to the pandas cheat sheet: https://pandas.pydata.org
Link to the dataset and code : https://github.com/vijita/pandas
Summary
Series looks like a one-dimensional Numpy array & DataFrame looks like a 2-dimensional array, but there are key differences.
Unlike Numpy array, the axes can be labeled in the Dataframe, and the data type of each column can be different in the Dataframe
Data Analysis using pandas (5 steps)
1. Define requirement
2. Data Collection
3. Exploratory Data analysis,
4. Data Wrangling and
5. Report results
Chapters
00:00 Pandas Introduction
01:46 Exploratory Data Analysis using Pandas
07:55 Data Wrangling using Pandas
Requirement: Find out the top 10 companies that increased in Marketcap from March to August.
Data collection : Two datasets from the companiesmarketcap.com at two different dates.
Exploratory Data analysis, also known as EDA. The goal of EDA is to understand the data and document the findings.
There are several methods to run EDA. Let’s follow these 5 steps
1. Observe data
2. Understand Columns
3. Find missing values
4. Data distribution
5. Identify the relationship and locate outliers
And then summarize the key findings.
Data Wrangling or Data Munging is the process of converting the raw data into a more useful form.
There are three steps here.
1. Data Cleaning
2. Data Merging and
3. Data Manipulation
Data cleaning is about fixing the anomalies in the data
Data manipulation is about applying logic and calculation to generate a completely different set of data.
With pandas, Data Wrangling or Data Munging is this easy!
Learning is fun
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