Introduction to NumPy
NumPy is another crucial library to work with numbers especially. In this chapter, we will learn how to use NumPy to find mean, median or mode values. But to use this library, you must first import it; the alias for NumPy is usually np. Look at the example: import numpy as np.
Imagine that we work with this simple list while learning functions:
data = [1, 12, 14, 50]
| Function title | Implementation | Result |
|---|---|---|
np.mean(data) | Finds the mean value of list | 19.25 |
np.median(data) | Finds the median value of list | 13.0 |
np.sum(data) | Finds the sum of all values in list | 77 |
np.size(data) | Finds the amount of values in list | 4 |
np.min(data) | Finds the minimum value of all values in list | 1 |
np.max(data) | Finds the maximim value of all values in list | 50 |
Aufgabe
Swipe to start coding
Your tasks here are to find the number of users, figure out the maximum and the minimum possible values of price column, and then find the median value of the price.
Follow the algorithm:
- Import the
numpywith thenpalias. - In the column
'user_id'find the size. - In the column
'price'find max and min values. - In the column
'price'find median value.
Lösung
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# Import the numpy
import numpy as np
import pandas as pd
# Read the file
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/783d7288-e86b-4b89-9966-a2fe97995277/section_2_dataset_upd.csv')
# Find the number of users
amount_of_users = np.size(df['user_id'])
# Figure out the maximum price
max_price = np.max(df['price'])
# Figure out the minimum price
min_price = np.min(df['price'])
# Figure out the median price
median_price = np.median(df['price'])
print(amount_of_users)
print(max_price)
print(min_price)
print(median_price)
War alles klar?
Danke für Ihr Feedback!
Abschnitt 3. Kapitel 10
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# Import the numpy
___
import pandas as pd
# Read the file
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/783d7288-e86b-4b89-9966-a2fe97995277/section_2_dataset_upd.csv')
# Find the amount of users
amount_of_users = np.___(df['___'])
# Figure out the maximum price
max_price = ___.___(df[___])
# Figure out the minimum price
min_price = ___(___)
# Figure out the median price
median_price = ___
print(amount_of_users)
print(max_price)
print(min_price)
print(median_price)
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