I have dataframe like this:
Date Location_ID Problem_ID
---------------------+------------+----------
2013-01-02 10:00:00 | 1 | 43
2012-08-09 23:03:01 | 5 | 2
...
How can I count how often a Problem occurs per day and per Location?
Use groupby
with converting Date
column to date
s or Grouper
with aggregate size
:
print (df)
Date Location_ID Problem_ID
0 2013-01-02 10:00:00 1 43
1 2012-08-09 23:03:01 5 2
#if necessary convert column to datetimes
df['Date'] = pd.to_datetime(df['Date'])
df1 = df.groupby([df['Date'].dt.date, 'Location_ID']).size().reset_index(name='count')
print (df1)
Date Location_ID count
0 2012-08-09 5 1
1 2013-01-02 1 1
Or:
df1 = (df.groupby([pd.Grouper(key='Date', freq='D'), 'Location_ID'])
.size()
.reset_index(name='count'))
If first column is index:
print (df)
Location_ID Problem_ID
Date
2013-01-02 10:00:00 1 43
2012-08-09 23:03:01 5 2
df.index = pd.to_datetime(df.index)
df1 = (df.groupby([df.index.date, 'Location_ID'])
.size()
.reset_index(name='count')
.rename(columns={'level_0':'Date'}))
print (df1)
Date Location_ID count
0 2012-08-09 5 1
1 2013-01-02 1 1
df1 = (df.groupby([pd.Grouper(level='Date', freq='D'), 'Location_ID'])
.size()
.reset_index(name='count'))
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