Importing Python Libraries¶
Notes:
import pandas
pandas.read_csv('cereal.csv')
name mfr type calories protein fat sodium fiber carbo sugars potass vitamins shelf weight cups rating
0 100% Bran N Cold 70 4 1 130 10.0 5.0 6 280 25 3 1.0 0.33 68.402973
1 100% Natural Bran Q Cold 120 3 5 15 2.0 8.0 8 135 0 3 1.0 1.00 33.983679
2 All-Bran K Cold 70 4 1 260 9.0 7.0 5 320 25 3 1.0 0.33 59.425505
3 All-Bran with Extra Fiber K Cold 50 4 0 140 14.0 8.0 0 330 25 3 1.0 0.50 93.704912
4 Almond Delight R Cold 110 2 2 200 1.0 14.0 8 1 25 3 1.0 0.75 34.384843
.. ... .. ... ... ... ... ... ... ... ... ... ... ... ... ... ...
72 Triples G Cold 110 2 1 250 0.0 21.0 3 60 25 3 1.0 0.75 39.106174
73 Trix G Cold 110 1 1 140 0.0 13.0 12 25 25 2 1.0 1.00 27.753301
74 Wheat Chex R Cold 100 3 1 230 3.0 17.0 3 115 25 1 1.0 0.67 49.787445
75 Wheaties G Cold 100 3 1 200 3.0 17.0 3 110 25 1 1.0 1.00 51.592193
76 Wheaties Honey Gold G Cold 110 2 1 200 1.0 16.0 8 60 25 1 1.0 0.75 36.187559
[77 rows x 16 columns]
Notes:
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('cereal.csv')
name mfr type calories protein fat sodium fiber carbo sugars potass vitamins shelf weight cups rating
0 100% Bran N Cold 70 4 1 130 10.0 5.0 6 280 25 3 1.0 0.33 68.402973
1 100% Natural Bran Q Cold 120 3 5 15 2.0 8.0 8 135 0 3 1.0 1.00 33.983679
2 All-Bran K Cold 70 4 1 260 9.0 7.0 5 320 25 3 1.0 0.33 59.425505
3 All-Bran with Extra Fiber K Cold 50 4 0 140 14.0 8.0 0 330 25 3 1.0 0.50 93.704912
4 Almond Delight R Cold 110 2 2 200 1.0 14.0 8 1 25 3 1.0 0.75 34.384843
.. ... .. ... ... ... ... ... ... ... ... ... ... ... ... ... ...
72 Triples G Cold 110 2 1 250 0.0 21.0 3 60 25 3 1.0 0.75 39.106174
73 Trix G Cold 110 1 1 140 0.0 13.0 12 25 25 2 1.0 1.00 27.753301
74 Wheat Chex R Cold 100 3 1 230 3.0 17.0 3 115 25 1 1.0 0.67 49.787445
75 Wheaties G Cold 100 3 1 200 3.0 17.0 3 110 25 1 1.0 1.00 51.592193
76 Wheaties Honey Gold G Cold 110 2 1 200 1.0 16.0 8 60 25 1 1.0 0.75 36.187559
[77 rows x 16 columns]
Notes:
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('cereal.csv'), we can shorten
it to pd.read_csv('cereal.csv').
from pandas import read_csv
read_csv('cereal.csv')
name mfr type calories protein fat sodium fiber carbo sugars potass vitamins shelf weight cups rating
0 100% Bran N Cold 70 4 1 130 10.0 5.0 6 280 25 3 1.0 0.33 68.402973
1 100% Natural Bran Q Cold 120 3 5 15 2.0 8.0 8 135 0 3 1.0 1.00 33.983679
2 All-Bran K Cold 70 4 1 260 9.0 7.0 5 320 25 3 1.0 0.33 59.425505
3 All-Bran with Extra Fiber K Cold 50 4 0 140 14.0 8.0 0 330 25 3 1.0 0.50 93.704912
4 Almond Delight R Cold 110 2 2 200 1.0 14.0 8 1 25 3 1.0 0.75 34.384843
.. ... .. ... ... ... ... ... ... ... ... ... ... ... ... ... ...
72 Triples G Cold 110 2 1 250 0.0 21.0 3 60 25 3 1.0 0.75 39.106174
73 Trix G Cold 110 1 1 140 0.0 13.0 12 25 25 2 1.0 1.00 27.753301
74 Wheat Chex R Cold 100 3 1 230 3.0 17.0 3 115 25 1 1.0 0.67 49.787445
75 Wheaties G Cold 100 3 1 200 3.0 17.0 3 110 25 1 1.0 1.00 51.592193
76 Wheaties Honey Gold G Cold 110 2 1 200 1.0 16.0 8 60 25 1 1.0 0.75 36.187559
[77 rows x 16 columns]
Notes:
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.