0 
from sklearn.datasets import make_classification df = make_classification(n_samples=10000, n_features=9, n_classes=1, random_state = 18, class_sep=2, n_informative=4)

after creating the data. it is tuple and after converting tuple into pandas dataframe

 df = pd.DataFrame(data, columns=["1","2","3","4","5","6","7","8","9"])

so i got 9 features (columns) but when i try to insert 9 cols it says.

ValueError: Shape of passed values is (2, 1), indices imply (2, 9)

Basically i wanna generate data and convert it into pandas dataframe but could not get to it. error is:

Improve this question

4 Answers 4

Reset to default
1

The first entry of the tuple contains the feature data and the the second entry contains the class labels. So if you want to make a pd.dataframe of the feature data you should use pd.DataFrame(df[0], columns=["1","2","3","4","5","6","7","8","9"]).

Improve this answer
Sign up to request clarification or add additional context in comments.

Comments

1

The make_classification returns a tuple with two NumPy arrays. Just use the first result of the tuple result.

Have a look at the return type in the Sklearn documentation.

import pandas as pd pd.DataFrame(df[0])

result:

 0 1 2 ... 6 7 8 0 1.223113 -1.962002 -0.288322 ... -2.152126 1.563291 2.790191 1 -0.239416 -3.782512 -1.587514 ... -0.519075 1.218147 -0.543413 2 -1.275076 -1.354999 -1.030673 ... -0.866303 1.915653 2.526826 3 -0.516765 -2.098868 -1.034506 ... 0.470277 1.917153 0.849975 4 -0.893197 -2.489030 1.012410 ... 3.562431 2.806255 -2.825570 ... ... ... ... ... ... ... ... 9995 -1.665167 -1.106121 -0.381195 ... 0.543236 2.406625 2.216029 9996 -0.783265 -1.405607 0.257606 ... -0.251951 2.167685 2.461260 9997 2.341676 -3.382589 -0.120150 ... 0.066099 2.453412 -0.758382 9998 -0.662257 -1.531187 -0.709562 ... 0.156203 2.495238 2.452315 9999 -0.756892 -4.895147 -0.385215 ... 0.898117 2.624591 -2.188389

Plus: There is a mismatch between the import and the usage:

!!! from sklearn.datasets import make_regression !!! df = make_classification(…)
Improve this answer

Comments

1

The make_classification from sklearn.datasets returns a tuple with two arrays. the first entry of this tuple is the value of features and the the second value is the results for each sample. This code converts the data into a data frame of Pandas.

from sklearn.datasets import make_classification import pandas as pd df= make_classification(n_samples=2, n_features=3, n_redundant=0, n_clusters_per_class=1, weights=[0.99], flip_y=0, random_state=1) df_list = list(df[1]) df = pd.DataFrame(df[0],columns=['Item1','Item2','Item3']) df = df.assign(Results= df_list )

this is the result of this code:

index Item1 Item2 Item3 Results
0 0.4542479646686235 -0.49801066491099416 1.3198274314649199 0
1 1.2497447542635314 -1.0534352345831717 0.5920838260510906 0
Improve this answer

1 Comment

How to Ask says no pictures when posting code. How to Answer probably as well! Please paste the output of print(df) instead.
0 
df = make_classification(n_samples=10000, n_features=9, n_classes=1, random_state = 18, class_sep=2, n_informative=4)

This line returns a tuple in which the first entry has Feature values or 'X' and the second entry has target values.

So, to make it a pandas data frame you have to make slice it like this,

df = pd.DataFrame(df[0], columns=["1","2","3","4","5","6","7","8","9"])

Full Code:

from sklearn.datasets import make_classification import pandas as pd df = make_classification(n_samples=10000, n_features=9, n_classes=1, random_state = 18, class_sep=2, n_informative=4) df = pd.DataFrame(df[0], columns=["1","2","3","4","5","6","7","8","9"]) print(df)

Output:

enter image description here

Improve this answer

Comments

Start asking to get answers

Find the answer to your question by asking.

Ask question

Explore related questions

See similar questions with these tags.