applying a user defined function to a dataframe

问题

The function im trying to write would take the dataframe provided and calculate the F statistic values and provide those as the output.

Data Format Final

Key      Color   Strength   Fabric  Sales
a         0         1         1       10
b         1         2         2       15

Here Color, strength and Fabric are independent while Sale is dependent.

The idea is to create a loop that creates a new dataframe for every unique key value: and perform a function over this dataframe and then create a new dataframe that is a concat of all the new dataframes obtained from unique key values

def regression():
    X=Final1.copy()
    y=Final1[['Sales']].copy()
    X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=.2, random_state=0)
    sel=f_classif(X_train, y_train)
    p_values=pd.Series(sel[0], index=X_train.columns)
    p_values=p_values.reset_index()
    pd.options.display.float_format = "{:,.2f}".format
    return p_values

Finals=[]
Finals=pd.DataFrame(Finals)
for group in Final.groupby('Key'): 
    # group is a tuple where the first value is the Key and the second is the dataframe
    Final1=group[1]
    Final1=pd.DataFrame(Final1)
    result=regression()
    Finals=pd.concat([Finals, result], axis=1)


# do xyz with result

print(Finals)

This is the code I came up with but its throwing an error

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-131-c3a3b53971d5> in <module>
      5     Final1=group[1]
      6     Final1=pd.DataFrame(Final1)
----> 7     result=regression()
      8     Finals=pd.concat([Finals, result], axis=1)
      9 

<ipython-input-120-d5c718baaba8> in regression()
      2     X=Final1.iloc[:,7:-1].copy()
      3     y=Final1[['Sale Rate']].copy()
----> 4     X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=.2, random_state=0)
      5     sel=f_classif(X_train, y_train)
      6     p_values=pd.Series(sel[0], index=X_train.columns)

~\anaconda3\lib\site-packages\sklearn\model_selection\_split.py in train_test_split(*arrays, **options)
   2120     n_samples = _num_samples(arrays[0])
   2121     n_train, n_test = _validate_shuffle_split(n_samples, test_size, train_size,
-> 2122                                               default_test_size=0.25)
   2123 
   2124     if shuffle is False:

~\anaconda3\lib\site-packages\sklearn\model_selection\_split.py in _validate_shuffle_split(n_samples, test_size, train_size, default_test_size)
   1803             'resulting train set will be empty. Adjust any of the '
   1804             'aforementioned parameters.'.format(n_samples, test_size,
-> 1805                                                 train_size)
   1806         )
   1807 

ValueError: With n_samples=1, test_size=0.2 and train_size=None, the resulting train set will be empty. Adjust any of the aforementioned parameters.

What could be going wrong with this code?

回答1:

A simple fix would be:

for group in Final.groupby('Key'): 
    # group is a tuple where the first value is the Key and the second is the dataframe
    result = regression(group[1])
    # do xyz with result

EDIT:

you do not have to convert group into a data frame again and can use it directly as it is already in the proper format.

# this line is not necessary
Final1 = pd.DataFrame(Final1)

judging from the error it is clear that the group that you have passed into the train_test_split function does not have enough records. which is quite evident in the error message. you will have to handle for such errors using try, except.

回答2:

Code works once I filter out all keys with less than 10 observations

来源：https://stackoverflow.com/questions/62327251/applying-a-user-defined-function-to-a-dataframe

标签

python

pandas

numpy

scikit-learn

regression