How do I get the rows where a certain value occurs? - pandas

Question

I have orders_df:
Symbol Order Shares
Date
2011-01-10 AAPL BUY 1500
2011-01-13 AAPL SELL 1500
2011-01-13 IBM BUY 4000
2011-01-26 GOOG BUY 1000
2011-02-02 XOM SELL 4000
2011-02-10 XOM BUY 4000
2011-03-03 GOOG SELL 1000
2011-03-03 IBM SELL 2200
2011-05-03 IBM BUY 1500
2011-06-03 IBM SELL 3300
2011-08-01 GOOG BUY 55
2011-08-01 GOOG SELL 55
I want have a variable that maps Date to the number of SELLS on that date. I also want a symmetric variable for BUY.
I tried doing it for all Orders by doing
num_orders_per_day = orders_df.groupby(['Date']).size()
and got:
Date
2011-01-10 1
2011-01-13 2
2011-01-26 1
2011-02-02 1
2011-02-10 1
2011-03-03 2
2011-05-03 1
2011-06-03 1
2011-08-01 2
but that is not the desired output.
What I want is sells_on_a_day:
2011-01-13 1
2011-02-02 1
2011-03-03 2
2011-06-03 1
2011-08-01 1
and then a similar buys_on_a_day variable.

Answer 1

First filter by boolean indexing and then get count:
num_sells_per_day = orders_df[orders_df['Order'] == 'SELL']
.groupby(level=0).size().reset_index(name='count')
print (num_sells_per_day)
Date count
0 2011-01-13 1
1 2011-02-02 1
2 2011-03-03 2
3 2011-06-03 1
4 2011-08-01 1
Alternative:
num_sells_per_day = orders_df.query("Order == 'SELL'")
.groupby(level=0)
.size()
.reset_index(name='count')
print (num_sells_per_day)
Date count
0 2011-01-13 1
1 2011-02-02 1
2 2011-03-03 2
3 2011-06-03 1
4 2011-08-01 1
Also is possible create 2 columns together, only get NaNs if some values missing:
df1 = orders_df.groupby(['Date','Order']).size().unstack()
print (df1)
Order BUY SELL
Date
2011-01-10 1.0 NaN
2011-01-13 1.0 1.0
2011-01-26 1.0 NaN
2011-02-02 NaN 1.0
2011-02-10 1.0 NaN
2011-03-03 NaN 2.0
2011-05-03 1.0 NaN
2011-06-03 NaN 1.0
2011-08-01 1.0 1.0