Indexes and Values
Series in pandas differ from numpy arrays in that they have indexes. These can be integers, floating-point numbers, strings, time series.
To get the series' indexes, use the .index attribute of a series object. To get values, use the values attribute. By default, indexes are integers starting from 0, but if you want to change them, you simply may reassign new list of indexes to the .index attribute. For instance,
12345678910111213# Importing library import pandas as pd # Creating pandas series ser = pd.Series([1000, 2500, 1700]) # Getting series' indexes and values print(ser.index) print(ser.values) print(ser) # Changing series' indexes ser.index = ['first', 'second', 'third'] print(ser)
As you can see, initial indexes were 0, 1, 2. After changing, they became 'first', 'second', 'third'.
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Series in pandas differ from numpy arrays in that they have indexes. These can be integers, floating-point numbers, strings, time series.
To get the series' indexes, use the .index attribute of a series object. To get values, use the values attribute. By default, indexes are integers starting from 0, but if you want to change them, you simply may reassign new list of indexes to the .index attribute. For instance,
12345678910111213# Importing library import pandas as pd # Creating pandas series ser = pd.Series([1000, 2500, 1700]) # Getting series' indexes and values print(ser.index) print(ser.values) print(ser) # Changing series' indexes ser.index = ['first', 'second', 'third'] print(ser)
As you can see, initial indexes were 0, 1, 2. After changing, they became 'first', 'second', 'third'.
¡Gracias por tus comentarios!
