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Maybe groupby is the wrong approach. Seems like it should work but I'm not seeing it...
I want to group an event by it's outcome. Here is my DataFrame (df):
Status Event
SUCCESS Run
SUCCESS Walk
SUCCESS Run
FAILED Walk
Here is my desired result:
Event SUCCESS FAILED
Run 2 1
Walk 0 1
I'm trying to make a grouped object but I can't figure out how to call it to display what I want.
grouped = df['Status'].groupby(df['Event'])
813
Use pandas DataFrame. groupby() to group the rows by column and use count() method to get the count for each group by ignoring None and Nan values. It works with non-floating type data as well.
The most simple method for pandas groupby count is by using the in-built pandas method named size(). It returns a pandas series that possess the total number of row count for each group. The basic working of the size() method is the same as len() method and hence, it is not affected by NaN values in the dataset.
groupby() can take the list of columns to group by multiple columns and use the aggregate functions to apply single or multiple aggregations at the same time.
Difference between rows or columns of a pandas DataFrame object is found using the diff() method. The axis parameter decides whether difference to be calculated is between rows or between columns. When the periods parameter assumes positive values, difference is found by subtracting the previous row from the next row.
try this:
pd.crosstab(df.Event, df.Status)
Status FAILED SUCCESS
Event
Run 0 2
Walk 1 1
len("df.groupby('Event').Status.value_counts().unstack().fillna(0)")
61
len("df.pivot_table(index='Event', columns='Status', aggfunc=len, fill_value=0)")
74
len("pd.crosstab(df.Event, df.Status)")
32
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