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Introduction to pandas [track]
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Kursusindhold

Introduction to pandas [track]

Introduction to pandas [track]

1. Basics
What is pandas?SeriesIndexes and ValuesChallengeDataFramesIndexes and ColumnsChallenge
2. Reading and Exploring Data
Reading CSV FilesReading XLS/XLSX FilesExploring Data [1/3]ChallengeExploring Data [2/3]Exploring Data [3/3]Challenge
3. Accessing DataFrame Values
Accessing Specific ColumnsChallengeAccessing Specific RowsChallengeAccessing Rows and ColumnsAccessing using .loc[] [1/3]Accessing using .loc[] [2/3]Accessing using .loc[] [3/3]
4. Aggregate Functions
Number of ObservationsUnique ObservationsCounting ValuesChallengeSum and ProductMean and MedianChallengeMinimum and Maximum

book
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)
copy

As you can see, initial indexes were 0, 1, 2. After changing, they became 'first', 'second', 'third'.

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Sektion 1. Kapitel 3

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course content

Kursusindhold

Introduction to pandas [track]

Introduction to pandas [track]

1. Basics
What is pandas?SeriesIndexes and ValuesChallengeDataFramesIndexes and ColumnsChallenge
2. Reading and Exploring Data
Reading CSV FilesReading XLS/XLSX FilesExploring Data [1/3]ChallengeExploring Data [2/3]Exploring Data [3/3]Challenge
3. Accessing DataFrame Values
Accessing Specific ColumnsChallengeAccessing Specific RowsChallengeAccessing Rows and ColumnsAccessing using .loc[] [1/3]Accessing using .loc[] [2/3]Accessing using .loc[] [3/3]
4. Aggregate Functions
Number of ObservationsUnique ObservationsCounting ValuesChallengeSum and ProductMean and MedianChallengeMinimum and Maximum

book
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)
copy

As you can see, initial indexes were 0, 1, 2. After changing, they became 'first', 'second', 'third'.

Var alt klart?

Hvordan kan vi forbedre det?

Tak for dine kommentarer!

Sektion 1. Kapitel 3
Vi beklager, at noget gik galt. Hvad skete der?
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