Different Aggregations
Lesson 16 of 19 in Coddy's Pandas Analytics course.
When using .sum() or any other similar method, it calculates the sum across all numeric columns. We can also specify for each column which calculation to do:
df.groupby('column_name').agg({'column_1': 'sum', 'column_2': 'mean'})
Challenge
EasyThe CSV file visits.csv contains information about how many visits were made at a particular location at a particular time.
Here is the first 5 lines of the file:
location_id,visits,timestamp
8,1771,27498
9,4187,79919
0,4959,54228
6,7721,41588Create a dataframe with the following columns: sum_visits and mean_timestamp, and their corresponding statistical calculations.
Make sure
sum_visitsis beforemean_timestamp..agg({column_1: '...', colunm_2: '...'})it not the same as.agg({column_2: '...', colunm_1: '...'}). This is because the columns order is reversed.
Save the result to df.
Try it yourself
# pandas as pd is already imported
df = pd.read_csv("./visits.csv")
# Write your code below
All lessons in Pandas Analytics
4Data Analysis with Pandas
Descriptive StatisticsGrouping and Aggregating DataDifferent AggregationsMerge & Concat2Working with the DataFrame
Understanding DataFramesAccessing DataData Cleaning - Missing dataData Cleaning - More tools3Data Manipulation with Pandas
Return Requested ResultFilter DataAdd & DeleteModify DataModify StringsCustom Modifications