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I have a dataframe, columns to be used are "sepal_length" and "sepal_width". I want to turn each single row into a single data point, like point1= [5.1 3.5] and point2 = [4.9 3] and so on. .to_numpy() just turns the whole 2 columns into large-sized numpy array, so it does not work for me. How can I work out?

user14416085
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  • does this solve your problem https://stackoverflow.com/questions/13187778/convert-pandas-dataframe-to-numpy-array – Joe Ferndz Oct 30 '20 at 06:35
  • Does this answer your question? [Mapping rows of a Pandas dataframe to numpy array](https://stackoverflow.com/questions/51468593/mapping-rows-of-a-pandas-dataframe-to-numpy-array) – Ruli Oct 30 '20 at 06:37

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You can simply use this:

nump_df=df.values
print(nump_df)

or

You can iterate over rows and convert each row to numpy array and append those arrays in a list. I hope the following code will help you:

point=[]
for idx, row in df.iterrows():
    p=row.to_numpy()
    point.append(p)

print(point)
Qamar Abbas
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