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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Indexes and Values
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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'
.
Obrigado pelo seu feedback!