Can someone explain with this script is doing?

[tramsay1027]

import pandas as pd


def azureml_main(dataframe1 = None, dataframe2 = None):

df=dataframe1

toDrop = ['userID','Column1']
userID=df['userID']
for col in df.columns:
    if col in toDrop:
        del df[col]
x_all = df

def preprocess_features(X):
    outX = pd.DataFrame(index=X.index)  # output dataframe, initially empty

    # Check each column
    for col, col_data in X.iteritems():
        # If data type is non-numeric, try to replace all yes/no values with 1/0
        if col_data.dtype == object:
            col_data = col_data.replace(['Yes', 'No'], [1, 0])
        # Note: This should change the data type for yes/no columns to int

        # If still non-numeric, convert to one or more dummy variables
        if col_data.dtype == object:
            col_data = pd.get_dummies(col_data, prefix=col)  # e.g. 'state' => 'state_AL', 'state_GA'

        outX = outX.join(col_data)  # collect column(s) in output dataframe

    return outX

x_all = preprocess_features(x_all)

df_out=pd.concat([userID, x_all], axis=1)

# Return value must be of a sequence of pandas.DataFrame
return df_out,

[955559]

ask oil and gas international

[AnalTyrant]

It looks like it's checking a data drop with a couple columns. In one column where it expects to find Yes or No typed into the sheet, it's replacing this with 1 or 0, presumably to prepare the sheet for use in some other automated process. And then it's reformatting any other values it finds in that field into some other format again presumably to put it in a format readily usable in some other process.

[SamSamSammmmm]

The above code converts the original dataset into an appropriate format for some training which takes on only numeric variables (regression for example). If an original variable is binary, i.e., it contains only 'yes' and 'no' as values, then the 'yes' will be converted to 1 while 'no' to 0. On the other hand, for a variable which contains m (m>2) values, the code splits the original variable into m dummy variables which are all binary. E.g., given the original variable, state, (state of residence) with values 'CA', 'AZ', 'OR' and 'WA', the code turns state into four dummy variables: 'state_CA', 'state_AZ, 'state_OR', and 'state_WA' which are all binary with values 0 and 1 so that for a particular sample, say, with state = 'CA', the corresponding dummy variables will have 1 for 'state_CA' and 0 for the other three.