Sort and Slice DataFrame in Pandas

Question

I have a dataframe like given below:

    detaildate  detailquantity
0   2012-02-09  7.0
1   2011-05-27  -1.0
2   2011-05-04  -2.0
3   2012-03-19  -2.0
4   2012-03-18  -3.0

I want to first sort the above dataframe by detaildate and then slice the dataframe from the first positive value of detailquantity to last index.

The result dataframe should look like this:

    detaildate  detailquantity
0   2012-02-09  7.0
4   2012-03-18  -3.0
3   2012-03-19  -2.0

I am trying the below code but it is resulting in an empty dataframe at the end and I am not able to figure out why

df.sort_values(by='detaildate', inplace=True)
df = df[df[df['detailquantity'] > 0].first_valid_index():]

What is wrong with the above code?

Answer 1

Use Series.cumsum with boolean mask and test all values greater like 0 , solution also working correctly if all negative values:

df.sort_values(by='detaildate', inplace=True)

df = df[(df['detailquantity'] > 0).cumsum() > 0]
print (df)
   detaildate  detailquantity
0  2012-02-09             7.0
4  2012-03-18            -3.0
3  2012-03-19            -2.0

Your solution should be changed by creating unique index, but is necessary at least one value matched:

df.sort_values(by='detaildate', inplace=True)
df = df.reset_index(drop=True)

df = df.loc[(df['detailquantity'] > 0).idxmax():]
print (df)
   detaildate  detailquantity
2  2012-02-09             7.0
3  2012-03-18            -3.0
4  2012-03-19            -2.0

Another alternative in numpy:

df.sort_values(by='detaildate', inplace=True)

df = df.iloc[(df['detailquantity'].values > 0).argmax():]
print (df)
   detaildate  detailquantity
0  2012-02-09             7.0
4  2012-03-18            -3.0
3  2012-03-19            -2.0