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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)
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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.
Solution
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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)
Swipe to start coding
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.
Solution
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