Converting Pandas Dataframe to numpy array [duplicate]

Question

The following is a Pandas dataframe df

index_column      value_column
 0                   20
 2                   28
 1                   30

It needs to be converted into a numpy array where index_column becomes the index of numpy array and value_column becomes the corresponding value.

That is np_arr[0]=20, np_arr[1]=30, np_arr[2]=28 and so on.

How can this be achieved?

And what happens when index_column isn't a RangeIndex starting from 0? numpy array indexing is 0 based, so do you need this to work in general for any arbitrary index arrangement? — ALollz
– ALollz, Commented Mar 13, 2019 at 18:21
index_column will have 0 to n rows,where n is number of rows in dataframe.But they may not be in ascending order. — TLanni
– TLanni, Commented Mar 13, 2019 at 18:25
1) this has been asked before, and 2) please don't follow any of the answers below if you're using 0.24. — cs95
– cs95, Commented Mar 13, 2019 at 18:31

Serge Ballesta · Accepted Answer · 2019-03-13 18:25:21Z

2

Pandas internally uses np.arrays. The values attribute is all you need:

df.value_column.values

is the np.array that you want.

answered Mar 13, 2019 at 18:25

Serge Ballesta

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Comments

Ashish kulkarni · Accepted Answer · 2019-03-13 18:30:00Z

2

np_arr = df.value_column.values

answered Mar 13, 2019 at 18:18

Ashish kulkarni

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@amandasmith: What you have mentioned is a good way to do it.

sourebh_s · Accepted Answer · 2019-03-13 18:31:01Z

1

np_arr = df.value_column.values

if your column name has special characters like space, use the following:

np_arr = df['temperature [Celsius]'].values

answered Mar 13, 2019 at 18:25

sourebh_s

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