Jan-26-2023, 07:01 PM
(This post was last modified: Jan-26-2023, 08:04 PM by deanhystad.)
That was interesting. At first no attachment, then Pop! up it appears.
The screenshot is fairly worthless. Post the code that generates these results. The code provides context for what you are trying to do.
Looking at sklearn.StandardScaler.fit_transform() documentation, it expects the first argument to be an "array-like" object. Did you already convert df to an array before calling fit_transform()?
Making a dataframe from an array is easy. It looks just like making a dataframe from a list. But why do you want to do this? From the tiny bit of code I can see, it looks like you change the fit_transform results to a dataframe just so you can write it to a csv file? That is unnecessary. It is easy to write a csv format file. You don't need pandas or csv to do it. This code writes a csv file using numpy,savetxt()
The screenshot is fairly worthless. Post the code that generates these results. The code provides context for what you are trying to do.
Looking at sklearn.StandardScaler.fit_transform() documentation, it expects the first argument to be an "array-like" object. Did you already convert df to an array before calling fit_transform()?
Making a dataframe from an array is easy. It looks just like making a dataframe from a list. But why do you want to do this? From the tiny bit of code I can see, it looks like you change the fit_transform results to a dataframe just so you can write it to a csv file? That is unnecessary. It is easy to write a csv format file. You don't need pandas or csv to do it. This code writes a csv file using numpy,savetxt()
import numpy as np
import pandas as pd
df = pd.DataFrame({"A": [i * 0.5 for i in range(1, 11)], "B": [i**0.5 for i in range(1, 11)]})
np.savetxt("test.csv", df.values, delimiter=",", header=",".join(df.columns), comments="")But if you really think you need to convert your results back to a dataframe, that is easy too.# Do not overwrite df. You will need it for getting column names results = scaler.fit_transform(df.values()) df2 = pd.DataFrame(results, index=df.index, columns=df.columns)