python - Iterating over columns and rows in pandas dataframe -

i trying iterate through dataframe have , use values inside of cells, need use names of columns , rows cells come from. because of doing following:

df=pandas.dataframe(data={"c1" : [1,2,3,4,5], "c2":[1,2,3,4,5]},                      index=["r1","r2","r3","r4","r5"]) row in df.index.values:     column in df.columns.values:        if (df[row][column] > 3:            if row in df2[column]:               print("data present") 

i need use row , column names because using them values in data frame has related information. know loops take forever in pandas, haven't been able find examples of how iterate on both row , column , same time. this:

df.applymap() 

wont work because gives value in cell, without keeping reference row , column cell in, , this:

df.apply(lambda row: row["column"]) 

wont work because need name of column without knowing before. this:

df.apply(lambda row: somefunction(row)) 

wont work because apply uses series object has row name, rather row , column names.

any insight helpful! running loop version takes forever , hogs cpu cores.

import pandas pd  df = pd.dataframe(data={"c1": [1, 2, 3, 4, 5],                          "c2": [1, 2, 3, 4, 5]},                    index=["r1", "r2", "r3", "r4", "r5"]) df2 = pd.dataframe({'r3': [1], 'r5': [1], 'r6': [1]}) 

to of corresponding columns df2 have value greater 3 in df, can use conditional list comprehension:

>>> [idx idx in df[df.gt(3).any(axis=1)].index if idx in df2] ['r5'] 

to see how works:

>>> df.gt(3)        c1     c2 r1  false  false r2  false  false r3  false  false r4   true   true r5   true   true 

then want index of row has value greater three:

df.gt(3).any(axis=1) out[23]:  r1    false r2    false r3    false r4     true r5     true dtype: bool  >>> df[df.gt(3).any(axis=1)]     c1  c2 r4   4   4 r5   5   5  >>> [i in df[df.gt(3).any(axis=1)].index] ['r4', 'r5']  >>> [i in df[df.gt(3).any(axis=1)].index if in df2] ['r5']