Get (row,col) indices of max value in dataframe

问题:

I have a data frame that looks something like this.

import pandas as pd
data = [[5, 7, 10], [7, 20, 4,], [8, 1, 6,]]
cities = ['Boston', 'Phoenix', 'New York']
df = pd.DataFrame(data, columns=cities, index=cities)

Output:

         Boston  Phoenix   New York
Boston      5       7         10
Phoenix     7       20         4
New York    8       1          6

And I want to be able to find the city pair with the greatest value. In this case I would want to return Phoenix,Phoenix.

I have tried:

cityMax = df.values.max()
cityPairs = df.idxmax()

The first one only gives me the largest value (20) and the second gives me each cities max pair not just the overall max. Is there a way to return the index and column header for a specified value in a dataframe?

回答1:

Use unstack() and extract the top MultiIndex as a tuple using idxmax()

import pandas as pd
data = [[5, 7, 10], [7, 20, 4,], [8, 1, 6,]]
cities = ['Boston', 'Phoenix', 'New York']
df = pd.DataFrame(data, columns=cities, index=cities)

print df.unstack().idxmax()

returns:

('Phoenix', 'Phoenix')

回答2:

You could try this too

In [15]: df_mat = df.as_matrix()

In [16]: cols, idxs = np.where(df_mat == np.amax(df_mat))

In [17]: ([df.columns[col] for col in cols], [df.index[idx] for idx in idxs])
Out[17]: (['Phoenix'], ['Phoenix'])

@piemont method seems more elegant. However, I wonder in your case (size of data), which method would work faster. Could you check that out, by timing these functions on your full data?

回答3:

row_city, column_city = (df.max(axis=1).idxmax(), df.max(axis=0).idxmax())