Learn Pandas Series

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What is a Series?

A Pandas Series is like a column in a table.

It is a one-dimensional array holding data of any type.

Example

Create a simple Pandas Series from a list:

        import pandas as pd

        a = [1, 7, 2]
        
        myvar = pd.Series(a)
        
        print(myvar)
      

Labels

If nothing else is specified, the values are labeled with their index number. First value has index 0, second value has index 1 etc.

This label can be used to access a specified value.

Example

Return the first value of the Series:

         
        print(myvar[0])
      

Create Labels

With the index argument, you can name your own labels.

Example

Create your own labels:

        import pandas as pd

        a = [1, 7, 2]
        
        myvar = pd.Series(a, index = ["x", "y", "z"])
        
        print(myvar)
      

When you have created labels, you can access an item by referring to the label.

Example

Return the value of "y":

        print(myvar["y"])
      

Key/Value Objects as Series

You can also use a key/value object, like a dictionary, when creating a Series.

Example

Create a simple Pandas Series from a dictionary:

        import pandas as pd

        calories = {"day1": 420, "day2": 380, "day3": 390}
        
        myvar = pd.Series(calories)
        
        print(myvar)
      

To select only some of the items in the dictionary, use the index argument and specify only the items you want to include in the Series.

Example

Create a Series using only data from "day1" and "day2":

        import pandas as pd

        calories = {"day1": 420, "day2": 380, "day3": 390}
        
        myvar = pd.Series(calories, index = ["day1", "day2"])
        
        print(myvar)
      

DataFrames

Data sets in Pandas are usually multi-dimensional tables, called DataFrames.

Series is like a column, a DataFrame is the whole table.

Example

Create a DataFrame from two Series:

        
        import pandas as pd

        data = {
          "calories": [420, 380, 390],
          "duration": [50, 40, 45]
        }
        
        myvar = pd.DataFrame(data)
        
        print(myvar)
      

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