Chapter 5 Series Deep Dive
Contents
Chapter 5 Series Deep Dive¶
import pandas as pd
url = 'https://github.com/mattharrison/datasets/raw/master/data/vehicles.csv.zip'
df = pd.read_csv(url, usecols = ['city08'])
city_mpg = df.city08
city_mpg
0 19
1 9
2 23
3 10
4 17
..
41139 19
41140 20
41141 18
41142 18
41143 16
Name: city08, Length: 41144, dtype: int64
Exercise 1¶
Explore the documentation for five attributes of a series from Jupyter
This produces a rather lengthy bit of output. As such, I’ve truncated it here.
dir(city_mpg)[:20]
['T',
'_AXIS_LEN',
'_AXIS_ORDERS',
'_AXIS_REVERSED',
'_AXIS_TO_AXIS_NUMBER',
'_HANDLED_TYPES',
'__abs__',
'__add__',
'__and__',
'__annotations__',
'__array__',
'__array_priority__',
'__array_ufunc__',
'__array_wrap__',
'__bool__',
'__class__',
'__contains__',
'__copy__',
'__deepcopy__',
'__delattr__']
I chose somewhat arbitrarily to look at:
Exercise 2¶
How many attributes are found on the .str attribute? Look at the documentation for three of them.
s_str = pd.Series(['a','b','c'])
n_attributes = len(dir(s_str.str))
print('There are {} attributes foiund on the .str attribute'.format(n_attributes))
There are 97 attributes foiund on the .str attribute
I chose somewhat arbitrarily to review the following:
Exercise 3¶
How many attributes are found on the .dt attribute? Look at the documentation for three of them.
s_ts = pd.to_datetime(pd.Series(['2022-01-01','2022-01-02','2022-01-03']))
n_attributes = len(dir(s_ts.dt))
print('There are {} attributes foiund on the .dt attribute'.format(n_attributes))
There are 83 attributes foiund on the .dt attribute
Once more, I chose somewhat arbitrarily to review the following: