学ぶ Group Data 2.0 | Explore Dataset

セクション 3. 章 9

single

メニューを表示するにはスワイプしてください

Let's imagine the situation where you want to group by job_title, but then you want to group by experience_level , for example.

Here, everything is so simple you need just put several columns in needed order to groupby function:


              1234567
            
import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/INTRO+to+Python/ds_salaries.csv', index_col = 0)

df = df.groupby(['job_title', 'experience_level']).mean()

print(df)

Look at the output.

output

By the way, if you don't want to group the whole table, you can specify the name of columns for which we should apply grouping. For instance, look at the previous code. If we want to calculate the mean value only for the 'salary' column, we specify needed columns, but do not forget about columns that should be grouped:


              1234567
            
import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/INTRO+to+Python/ds_salaries.csv', index_col = 0)

df = df[['salary','job_title', 'experience_level']].groupby(['job_title', 'experience_level']).mean()

print(df)

Be careful; if you want to work with several columns, you have to put them into [[]] (look at the example).

Look at the result:

result

タスク

スワイプしてコーディングを開始

Your task here is to work with the known dataset and count amount of users for each plan depending on the status of their trial. To do it, follow the algorithm:

Group by 'plan' column, then by 'trial' column, using only three columns: 'user_id', 'plan', 'trial'. Apply the count() function.
Print the df using only print() function.

解答

実践的な練習のためにデスクトップに切り替える下記のオプションのいずれかを利用して、現在の場所から続行する

すべて明確でしたか？

フィードバックありがとうございます！

セクション 3. 章 9

single

AIに質問する

何でも質問するか、提案された質問の1つを試してチャットを始めてください