What is Data Preprocessing?

As a data analyst, most likely you will deal with 'dirty' data. What issues can happen while working with gathered data?

Missing values
Wrong data types
Outliers
Other inconsistency

Within the course, you will be aquintated how to detect and fix common issues. Let's start with the dataset that you will use throughout the course. It will be the Statvillage dataset containing data on a hypothetical village in Canada. There are more than 40 columns in the dataset, but we will stop only on about 10 of them.

The village consists of 128 blocks of 8 houses each. First, let's read the data and see how does it look like.


              123456
            
# Importing the library
import pandas as pd

# Reading the file
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/f2947b09-5f0d-4ad9-992f-ec0b87cd4b3f/data.csv')
print(df)

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Cursusinhoud

Data Manipulation using pandas

1. Preprocessing Data: Part I

What is Data Preprocessing?Types consistency Poor Data Presentation Manipulating Strings Challenge Replacing Specific Elements Simultaneous Replacement Challenge

2. Preprocessing Data: Part II

Logical Inconsistency Removing Rows Challenge Outliers Challenge Missing Values Filling NA values Challenge

3. Grouping Data

What is Grouping Data?Grouping in pandas [1/2]Challenge Grouping in pandas [2/2]Challenge Grouping by Several Columns Challenge

4. Aggregating and Visualizing Data

Advanced Aggregation [1/2]Challenge Advanced Aggregation [2/2]Challenge Histograms Challenge Bar and Scatter Plots Other Types of Graphs Challenge 1 Challenge 2

5. Joining Data

What is Joining Data?Left Join Right Join Inner Join Outer Join Concatenation