Introduction to Pandas
Chapter 4: Accessing DataFrame rows and columns
Accessing specific rows and columns
You can also access specific rows and/or columns.
For example, you can access all rows, and a single column.
>>> print(df.loc[:, "Type 1"])
Name
Bulbasaur Grass
Ivysaur Grass
Venusaur Grass
VenusaurMega Venusaur Grass
Charmander Fire
...
Diancie Rock
DiancieMega Diancie Rock
HoopaHoopa Confined Psychic
HoopaHoopa Unbound Psychic
Volcanion Fire
Name: Type 1, Length: 800, dtype: object
The following example accesses all rows, and multiple columns.
>>> print(df.loc[:, ["Type 1", "Generation"]])
Name
Bulbasaur Grass 1
Ivysaur Grass 1
Venusaur Grass 1
VenusaurMega Venusaur Grass 1
Charmander Fire 1
... ... ...
Diancie Rock 6
DiancieMega Diancie Rock 6
HoopaHoopa Confined Psychic 6
HoopaHoopa Unbound Psychic 6
Volcanion Fire 6
[800 rows x 2 columns]
Next, we have an example of two rows and two columns.
>>> print(df.loc[["Squirtle", "Pikachu"], ["Type 1", "Generation"]])
Type 1 Generation
Name
Squirtle Water 1
Pikachu Electric 1
You can also access multiple rows and columns by the row/column position.
>>> print(df.iloc[1:3, -4:-2]) # Multiple rows and columns by position
Sp. Def Speed
Name
Ivysaur 80 60
Venusaur 100 80