Importing Python Libraries

import pandas

pandas.read_csv('data/cereal.csv')

	name	mfr	type	calories	...	shelf	weight	cups	rating
0	100% Bran	N	Cold	70	...	3	1.0	0.33	68.402973
1	100% Natural Bran	Q	Cold	120	...	3	1.0	1.00	33.983679
2	All-Bran	K	Cold	70	...	3	1.0	0.33	59.425505
...	...	...	...	...	...	...	...	...	...
74	Wheat Chex	R	Cold	100	...	1	1.0	0.67	49.787445
75	Wheaties	G	Cold	100	...	1	1.0	1.00	51.592193
76	Wheaties Honey Gold	G	Cold	110	...	1	1.0	0.75	36.187559

77 rows × 16 columns

All the way back in Module 1, we learned how to import the pandas library for dataframe wrangling and altair to visualize our data with plots.

We imported these libraries because basic Python does not have all the built-in tools that we need to accomplish what we want; therefore, we import other tools into our environment.

To import a library, we saw that we can use the keyword import followed by the desired package name.

In this case, we are importing pandas.

This now lets us use verbs that reside in the pandas library, such as read_csv().

We need to specify the library name -pandas and then the verb - read_csv().

import pandas as pd 
import altair as alt

pd.read_csv('data/cereal.csv')

	name	mfr	type	calories	...	shelf	weight	cups	rating
0	100% Bran	N	Cold	70	...	3	1.0	0.33	68.402973
1	100% Natural Bran	Q	Cold	120	...	3	1.0	1.00	33.983679
2	All-Bran	K	Cold	70	...	3	1.0	0.33	59.425505
...	...	...	...	...	...	...	...	...	...
74	Wheat Chex	R	Cold	100	...	1	1.0	0.67	49.787445
75	Wheaties	G	Cold	100	...	1	1.0	1.00	51.592193
76	Wheaties Honey Gold	G	Cold	110	...	1	1.0	0.75	36.187559

77 rows × 16 columns

For efficiency, in the majority of this course, we have been importing our libraries by assigning them a shorter condensed name or alias.

For example, in the assignments and practice exercises, we have been importing pandas and altair with names such as pd and alt, respectively.

Now when we call functions from either of these libraries, we only type the short form alias we assigned to the library name.

Now instead of writing pandas.read_csv('data/cereal.csv'), we can shorten it to pd.read_csv('data/cereal.csv').

from pandas import read_csv

read_csv('data/cereal.csv')

	name	mfr	type	calories	...	shelf	weight	cups	rating
0	100% Bran	N	Cold	70	...	3	1.0	0.33	68.402973
1	100% Natural Bran	Q	Cold	120	...	3	1.0	1.00	33.983679
2	All-Bran	K	Cold	70	...	3	1.0	0.33	59.425505
...	...	...	...	...	...	...	...	...	...
74	Wheat Chex	R	Cold	100	...	1	1.0	0.67	49.787445
75	Wheaties	G	Cold	100	...	1	1.0	1.00	51.592193
76	Wheaties Honey Gold	G	Cold	110	...	1	1.0	0.75	36.187559

77 rows × 16 columns

We can also import a single function from a library using the keyword from.

If we only want the read_csv() function from the pandas package, we could first specify the library the function belongs to, followed by the function name:

Here it’s from pandas import read_csv.

Now when we call read_csv(), we don’t need to specify the package name or alias before it.

This mostly helps if we have only a single function we wish to use, instead of importing the entire library.

This works for Python libraries, but how do we import functions we’ve made that are located in another file?

If we want to reuse code to adhere to the DRY principle, what is our next step?

This question will be answered in the next section of this module.

Let’s apply what we learned!