Merging results with model.predict () with the original pandas DataFrame?

Question

I am trying to combine the results of a method predictwith the source data in an object pandas.DataFrame.

from sklearn.datasets import load_iris
from sklearn.cross_validation import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
import numpy as np

data = load_iris()

# bear with me for the next few steps... I'm trying to walk you through
# how my data object landscape looks... i.e. how I get from raw data 
# to matrices with the actual data I have, not the iris dataset
# put feature matrix into columnar format in dataframe
df = pd.DataFrame(data = data.data)

# add outcome variable
df['class'] = data.target

X = np.matrix(df.loc[:, [0, 1, 2, 3]])
y = np.array(df['class'])

# finally, split into train-test
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size = 0.8)

model = DecisionTreeClassifier()

model.fit(X_train, y_train)

# I've got my predictions now
y_hats = model.predict(X_test)

To combine these predictions with the original df, I try this:

df['y_hats'] = y_hats

But it increases:

ValueError: the length of the values ​​does not match the length of the index

, df train_df test_df, , , X y (my , ). df, y_hats , , , X_test y_test, ? , ? , train, np.nan .

Answer 1

y_hats (20%), X_test. , ( X_test X_test), (X). :

y_hats2 = model.predict(X)

df['y_hats'] = y_hats2

, , , , datset

from sklearn.datasets import load_iris
from sklearn.cross_validation import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
import numpy as np

data = load_iris()

# bear with me for the next few steps... I'm trying to walk you through
# how my data object landscape looks... i.e. how I get from raw data 
# to matrices with the actual data I have, not the iris dataset
# put feature matrix into columnar format in dataframe
df = pd.DataFrame(data = data.data)

# add outcome variable
df_class = pd.DataFrame(data = data.target)

# finally, split into train-test
X_train, X_test, y_train, y_test = train_test_split(df,df_class, train_size = 0.8)

model = DecisionTreeClassifier()

model.fit(X_train, y_train)

# I've got my predictions now
y_hats = model.predict(X_test)

y_test['preds'] = y_hats

df_out = pd.merge(df,y_test[['preds']],how = 'left',left_index = True, right_index = True)

Answer 2

, :

data['y_hats'] = y_hats
data.to_csv('data1.csv')

Answer 3

()

...
.
.
.
X_train, X_test, y_train, y_test = train_test_split(df,df_class, train_size = 0.8)

model = DecisionTreeClassifier()

model.fit(X_train, y_train)

y_hats = model.predict(X_test)

y_hats  = pd.DataFrame(y_hats)

df_out = X_test.reset_index()
df_out["Actual"] = y_test.reset_index()["Columns_Name"]
df_out["Prediction"] = y_hats.reset_index()[0]


y_test['preds'] = y_hats

df_out = pd.merge(df,y_test[['preds']],how = 'left',left_index = True, right_index = True)

Answer 4

you can also use

y_hats = model.predict(X)

df['y_hats'] = y_hats.reset_index()['name of the target column']