Working with pivot tables
Data Manipulation with pandas
Richie Cotton
Data Evangelist at DataCamp
A bigger dog dataset
print(dog_pack)
breed color height_cm weight_kg
0 Boxer Brown 62.64 30.4
1 Poodle Black 46.41 20.4
2 Beagle Brown 36.39 12.4
3 Chihuahua Tan 19.70 1.6
4 Labrador Tan 54.44 36.1
.. ... ... ... ...
87 Boxer Gray 58.13 29.9
88 St. Bernard White 70.13 69.4
89 Poodle Gray 51.30 20.4
90 Beagle White 38.81 8.8
91 Beagle Black 33.40 13.5
Pivoting the dog pack
dogs_height_by_breed_vs_color = dog_pack.pivot_table(
"height_cm", index="breed", columns="color")
print(dogs_height_by_breed_vs_color)
color Black Brown Gray Tan White
breed
Beagle 34.500000 36.4500 36.313333 35.740000 38.810000
Boxer 57.203333 62.6400 58.280000 62.310000 56.360000
Chihuahua 18.555000 NaN 21.660000 20.096667 17.933333
Chow Chow 51.262500 50.4800 NaN 53.497500 54.413333
Dachshund 21.186667 19.7250 NaN 19.375000 20.660000
Labrador 57.125000 NaN NaN 55.190000 55.310000
Poodle 48.036000 57.1300 56.645000 NaN 44.740000
St. Bernard 63.920000 65.8825 67.640000 68.334000 67.495000
.loc[] + slicing is a power combo
dogs_height_by_breed_vs_color.loc["Chow Chow":"Poodle"]
color Black Brown Gray Tan White
breed
Chow Chow 51.262500 50.480 NaN 53.4975 54.413333
Dachshund 21.186667 19.725 NaN 19.3750 20.660000
Labrador 57.125000 NaN NaN 55.1900 55.310000
Poodle 48.036000 57.130 56.645 NaN 44.740000
The axis argument
dogs_height_by_breed_vs_color.mean(axis="index")
color
Black 43.973563
Brown 48.717917
Gray 48.107667
Tan 44.934738
White 44.465208
dtype: float64
Calculating summary stats across columns
dogs_height_by_breed_vs_color.mean(axis="columns")
breed
Beagle 36.362667
Boxer 59.358667
Chihuahua 19.561250
Chow Chow 52.413333
Dachshund 20.236667
Labrador 55.875000
Poodle 51.637750
St. Bernard 66.654300
dtype: float64
Let's practice!
Data Manipulation with pandas
