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I want to get the count of dataframe rows based on conditional selection. I tried the following code.
print df[(df.IP == head.idxmax()) & (df.Method == 'HEAD') & (df.Referrer == '"-"')].count() output:
IP 57 Time 57 Method 57 Resource 57 Status 57 Bytes 57 Referrer 57 Agent 57 dtype: int64 The output shows the count for each an every column in the dataframe. Instead I need to get a single count where all of the above conditions satisfied? How to do this? If you need more explanation about my dataframe please let me know.
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Utilizing the Len() Method with One Condition We will first apply the condition on a single column to retrieve the number of rows that matches the condition. Then, we apply it to the multiple columns of the DataFrame. For both techniques, we utilized the “len()” method of Pandas.
Get Number of Rows in DataFrame You can use len(df. index) to find the number of rows in pandas DataFrame, df. index returns RangeIndex(start=0, stop=8, step=1) and use it on len() to get the count.
The results show that apply massively outperforms iterrows . As mentioned previously, this is because apply is optimized for looping through dataframe rows much quicker than iterrows does. While slower than apply , itertuples is quicker than iterrows , so if looping is required, try implementing itertuples instead.
You are asking for the condition where all the conditions are true, so len of the frame is the answer, unless I misunderstand what you are asking
In [17]: df = DataFrame(randn(20,4),columns=list('ABCD')) In [18]: df[(df['A']>0) & (df['B']>0) & (df['C']>0)] Out[18]: A B C D 12 0.491683 0.137766 0.859753 -1.041487 13 0.376200 0.575667 1.534179 1.247358 14 0.428739 1.539973 1.057848 -1.254489 In [19]: df[(df['A']>0) & (df['B']>0) & (df['C']>0)].count() Out[19]: A 3 B 3 C 3 D 3 dtype: int64 In [20]: len(df[(df['A']>0) & (df['B']>0) & (df['C']>0)]) Out[20]: 3 If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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