Data Manipulation using pandas

Data Manipulation using pandas

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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

Replacing Specific Elements

Simultaneous Replacement

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

Filling NA values

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]

Grouping in pandas [2/2]

Grouping by Several Columns

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]

Advanced Aggregation [2/2]

Bar and Scatter Plots

Other Types of Graphs

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?

Course Content

Data Manipulation using pandas

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

Filling NA values

As you remember, removing all the rows with NA values is not always the best solution. Let's learn another method, how to fill NA values in a dataframe.

If you want to fill NAs with specific values, use the .fillna() method. There are many options on what parameters you should use. Let's consider obligatory and the most appropriate ones:

value - defines value that should be set instead of NAs. Can be a dictionary in format {'column_name': value_to_replace}.
method - defines the method that should be used to fill NA values (for instance, 'ffill': propagate last valid observation forward to next valid backfill / 'bfill': use next valid observation to fill gap.
limit - number of consequtive NA values that should be filled. By default, this parameter isn't specified. For instance, we can fill the NA values within the 'omphtotinch', 'hmwkswk' columns with respective means.


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# 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')
# NA values before deleting
print("Before filling:", df[['omphtotinch', 'hmwkswk']].isna().sum())
# Fill NA values with respective means
df.fillna(value = {'omphtotinch': df.morgh.mean(), 'hmwkswk': df.hmwkswk.mean()}, inplace = True)
# NA values after filling
print("After filling:", df[['omphtotinch', 'hmwkswk']].isna().sum())

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Section 2. Chapter 7

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