CODEWITHSUNDEEP
pandaspython-3.5
Zanam
Zanam

Reputation: 4807

Getting maximum from dataframe for values from another dataframe

I have a dataframe with temperature as:

temp.ix[1:10]
                     KCRP
DateTime                 
2011-01-01 01:00:00  61.0
2011-01-01 02:00:00  60.0
2011-01-01 03:00:00  57.0
2011-01-01 04:00:00  56.0
2011-01-01 05:00:00  51.0
2011-01-01 06:00:00  55.0
2011-01-01 07:00:00  65.0
2011-01-01 08:00:00  55.0
2011-01-01 09:00:00  55.0

I have another dataframe df as:

df[['Start Time', 'End Time']].ix[1:10]
                           Start Time              End Time
DateTime                                                   
2011-01-23 05:00:00 2011-01-01 05:00:00 2011-01-01 06:11:00
2011-01-25 04:00:00 2011-01-25 04:51:00 2011-01-26 00:19:00
2011-01-26 04:00:00 2011-01-26 04:29:00 2011-01-26 23:13:00
2011-02-03 07:00:00 2011-02-03 07:56:00 2011-02-03 08:11:00
2011-02-12 19:00:00 2011-02-12 19:52:00 2011-02-13 12:14:00
2011-02-15 14:00:00 2011-02-15 14:09:00 2011-02-15 14:22:00
2011-02-22 05:00:00 2011-02-22 05:47:00 2011-02-22 05:55:00
2011-02-26 06:00:00 2011-02-26 06:47:00 2011-02-26 07:25:00
2011-03-01 00:00:00 2011-03-01 00:44:00 2011-03-02 00:11:00

For each row of df, I want to select the maximum value from temp where from temp I extract all values between and including Start Time and End Time.

So, for first row of df my answer will be as:

df[['Start Time', 'End Time']].ix[1:10]
                           Start Time              End Time   Max Temp
DateTime                                                   
2011-01-23 05:00:00 2011-01-01 05:00:00 2011-01-01 06:11:00   55

I am not sure how to proceed with this other than looping through each row of df which is probably not an interesting way to do it.

I have tried:

[np.max(temp[(temp.index >= x[0]) & (temp.index <= x[1])])['KCRP] for x in
                      zip(df['Start Time'], df['End Time'])]

Upvotes: 0

Views: 40

Answers (1)

panktijk
panktijk

Reputation: 1614

A simple way wouold be to do this using apply:

def get_max_temp(row):
    return max(temp[(temp['DateTime'] >= row['Start_Time']) & (temp['DateTime'] <= row['End_Time'])]['KCRP'])

df['Max_Temp'] = df.apply(get_max_temp, axis=1)

You can also use a vectorized function for better performance, but explicitly iterating over rows in a dataframe should almost always be the last option.

UPDATE:

Vector version:

def get_max_temp(start, end):
    return max(temp[(temp['DateTime'] >= start) & (temp['DateTime'] <= end)]['KCRP'])

get_max_temp = np.vectorize(get_max_temp)
df['Max_Temp'] = get_max_temp(df['Start_Time'], df['End_Time'])

Upvotes: 1

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