Course Content

Preprocessing Data

1. Data Exploration

Data Extracting Explore the Dataset Data Types

2. Data Cleaning

Missing and Wrong Data Work with NaNs Replace Numerical Missing Data with Values Dealing with Missing Ages Replace Categorical Missing Data with Values Replace Missing Values with Interpolation Removing Duplicates Removing Outliers Visualizing Outliers

3. Data Validation

Types Conversion Data Binning

4. Normalization & Standardization

Data Normalization Data Standardization

5. Data Encoding

Label Encoding One Hot Encoding

Explore the Dataset

Before you start, it's important to take a look at the data you'll work with. There is a list of useful methods which can be applied to the pandas dataframes:


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# info about the dataframe shape, data types
data.info()
# the size of the dataframe
data.shape
# list of the columns
data.columns 
# returns all distinct values containing in the column called ColumnName
data['ColumnName'].unique() 
# returns the metrics: mean, mode, min, max etc.
data.describe() 

# returns top 5 rows
data.head() 
# returns top 10 rows (or any other number you'll pass)
data.head(10) 
# returns bottom 5 rows
data.tail() 
# returns bottom 10 rows (or any other number)
data.tail(10) 
# returns 10 random rows
data.sample(10)

Task

For given dataset data, extract and print 5 rows using sample() function.

Find all the columns' names and put them to the cols variable.

Find the unique values for each column and output these values.

Task

For given dataset data, extract and print 5 rows using sample() function.

Find all the columns' names and put them to the cols variable.

Find the unique values for each column and output these values.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 1. Chapter 2

Explore the Dataset

Before you start, it's important to take a look at the data you'll work with. There is a list of useful methods which can be applied to the pandas dataframes:


              123456789101112131415161718192021
            
# info about the dataframe shape, data types
data.info()
# the size of the dataframe
data.shape
# list of the columns
data.columns 
# returns all distinct values containing in the column called ColumnName
data['ColumnName'].unique() 
# returns the metrics: mean, mode, min, max etc.
data.describe() 

# returns top 5 rows
data.head() 
# returns top 10 rows (or any other number you'll pass)
data.head(10) 
# returns bottom 5 rows
data.tail() 
# returns bottom 10 rows (or any other number)
data.tail(10) 
# returns 10 random rows
data.sample(10)

Task

For given dataset data, extract and print 5 rows using sample() function.

Find all the columns' names and put them to the cols variable.

Find the unique values for each column and output these values.

Task

For given dataset data, extract and print 5 rows using sample() function.

Find all the columns' names and put them to the cols variable.

Find the unique values for each column and output these values.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 1. Chapter 2

Explore the Dataset

Before you start, it's important to take a look at the data you'll work with. There is a list of useful methods which can be applied to the pandas dataframes:


              123456789101112131415161718192021
            
# info about the dataframe shape, data types
data.info()
# the size of the dataframe
data.shape
# list of the columns
data.columns 
# returns all distinct values containing in the column called ColumnName
data['ColumnName'].unique() 
# returns the metrics: mean, mode, min, max etc.
data.describe() 

# returns top 5 rows
data.head() 
# returns top 10 rows (or any other number you'll pass)
data.head(10) 
# returns bottom 5 rows
data.tail() 
# returns bottom 10 rows (or any other number)
data.tail(10) 
# returns 10 random rows
data.sample(10)

Task

For given dataset data, extract and print 5 rows using sample() function.

Find all the columns' names and put them to the cols variable.

Find the unique values for each column and output these values.

Task

For given dataset data, extract and print 5 rows using sample() function.

Find all the columns' names and put them to the cols variable.

Find the unique values for each column and output these values.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Before you start, it's important to take a look at the data you'll work with. There is a list of useful methods which can be applied to the pandas dataframes:


              123456789101112131415161718192021
            
# info about the dataframe shape, data types
data.info()
# the size of the dataframe
data.shape
# list of the columns
data.columns 
# returns all distinct values containing in the column called ColumnName
data['ColumnName'].unique() 
# returns the metrics: mean, mode, min, max etc.
data.describe() 

# returns top 5 rows
data.head() 
# returns top 10 rows (or any other number you'll pass)
data.head(10) 
# returns bottom 5 rows
data.tail() 
# returns bottom 10 rows (or any other number)
data.tail(10) 
# returns 10 random rows
data.sample(10)

Task

For given dataset data, extract and print 5 rows using sample() function.

Find all the columns' names and put them to the cols variable.

Find the unique values for each column and output these values.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 1. Chapter 2

Switch to desktop for real-world practiceContinue from where you are using one of the options below