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Data Manipulation using pandas

Challenge

Let's find out the average prices for each pair of number of rooms - number of bedrooms in a dwelling.

Task

Swipe to start coding

Group the values in the df dataframe by values of 'roomh', 'broomh' columns.
Select the 'valueh' column.
Calculate means for each group.

Solution

# 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/data4.csv')

# Grouping and aggregating data

print(df.groupby(['roomh', 'broomh'])['valueh'].mean())

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Section 3. Chapter 7

# 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/data4.csv')

# Grouping and aggregating data

print(df.___([___])___.___())

Data Manipulation using pandas

1. Preprocessing Data: Part I

The data received from different sources can be messy, and to use it in the future, you must ensure it is convenient. In the first section, you will learn what data preprocessing is and will deal with some logical inconsistencies.

What is Data Preprocessing?

Types consistency

Poor Data Presentation

Manipulating Strings

Challenge

Replacing Specific Elements

Simultaneous Replacement

Challenge

2. Preprocessing Data: Part II

Missing or NA values, outliers, and inconsistencies are other types of problematic data. Throughout the second section of the course, you will learn how to deal with such issues.

Logical Inconsistency

3. Grouping Data

As a data analyst, you will need to draw compact conclusions based on large amounts of data. In order to achieve that, you need to understand the data grouping idea and how to apply it to examples.

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

Sometimes one built-in function is not enough to draw a complete conclusion, so you need to use something more complex. This section will teach you how to apply multiple functions while grouping. Also, you will learn to visualize data using the pandas library only and will be acquainted with the main plot types. As a data analyst, you will need to draw compact conclusions based on large amounts of data. In order to achieve that, you need to understand the data grouping idea and how to apply it to examples.

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

As mentioned before, sometimes you may need to work with data received from multiple sources. This section will teach you how to join two dataframes using different techniques.

What is Joining Data?