CODEWITHSUNDEEP
pythonpandasnumpydataframe
Cedric H.
Cedric H.

Reputation: 8288

Store numpy.array in cells of a Pandas.DataFrame

I have a dataframe in which I would like to store 'raw' numpy.array:

df['COL_ARRAY'] = df.apply(lambda r: np.array(do_something_with_r), axis=1)

but it seems that pandas tries to 'unpack' the numpy.array.

Is there a workaround? Other than using a wrapper (see edit below)?

I tried reduce=False with no success.

EDIT

This works, but I have to use the 'dummy' Data class to wrap around the array, which is unsatisfactory and not very elegant.

class Data:
    def __init__(self, v):
        self.v = v

meas = pd.read_excel(DATA_FILE)
meas['DATA'] = meas.apply(
    lambda r: Data(np.array(pd.read_csv(r['filename'])))),
    axis=1
)

Upvotes: 76

Views: 162846

Answers (9)

yuzhen_3301
yuzhen_3301

Reputation: 19

Suppose you have a DataFrame ds and it has a column named 'class'. If ds['class'] contains strings or numbers, and you want to change them with numpy.ndarrays or lists, the following code would help. In the code, class2vector is a numpy.ndarray or list and ds_class is a filter condition.

ds['class'] = ds['class'].map(lambda x: class2vector if (isinstance(x, str) and (x == ds_class)) else x)

Upvotes: 1

Begoodpy
Begoodpy

Reputation: 1084

Preset the type of your column to object, this will allow you to store a NumPy array as-is:

df['COL_ARRAY'] = pd.Series(dtype='object')

df['COL_ARRAY'] = df.apply(lambda r: np.array(do_something_with_r), axis=1)

Upvotes: 2

Jérémy Talbot-Pâquet
Jérémy Talbot-Pâquet

Reputation: 371

If you only want some of the columns you could do something lie this. Taking the example of @allenyllee,

df = pd.DataFrame({'id': [1, 2, 3, 4],
                   'a': ['on', 'on', 'off', 'off'],
                   'b': ['on', 'off', 'on', 'off']})

df['new'] = df[['a','b']].apply(lambda x: np.array(x), axis=1)

which outputs

   id    a    b         new
0   1   on   on    [on, on]
1   2   on  off   [on, off]
2   3  off   on   [off, on]
3   4  off  off  [off, off]

you can also change the order of [['a', 'b']] if you need a specific order.

Upvotes: 0

Marcelo
Marcelo

Reputation: 65

Here goes my 2 cents contribution (tested on Python 3.7):

import pandas as pd
import numpy as np

dataArray = np.array([0.0, 1.0, 2.0])
df = pd.DataFrame()
df['User Col A'] = [1]
df['Array'] = [dataArray]

Upvotes: 0

Kenneth
Kenneth

Reputation: 31

choose eval buildin function is easy to use and easy to read.

# First ensure use object store str
df['col2'] = self.df['col2'].astype(object)
# read
arr_obj = eval(df.at[df[df.col_1=='xyz'].index[0], 'col2']))
# write
df.at[df[df.col_1=='xyz'].index[0], 'col2'] = str(arr_obj)

real store display perfect human readable value:

col_1,  col_2
xyz,    "['aaa', 'bbb', 'ccc', 'ddd']"

Upvotes: 1

David Wasserman
David Wasserman

Reputation: 611

If you first set a column to have type object, you can insert an array without any wrapping:

df = pd.DataFrame(columns=[1])
df[1] = df[1].astype(object)
df.loc[1, 1] = np.array([5, 6, 7, 8])
df

Output:

    1
1   [5, 6, 7, 8]

Upvotes: 33

allenyllee
allenyllee

Reputation: 1074

Just wrap what you want to store in a cell to a list object through first apply, and extract it by index 0of that list through second apply:

import pandas as pd
import numpy as np

df = pd.DataFrame({'id': [1, 2, 3, 4],
                   'a': ['on', 'on', 'off', 'off'],
                   'b': ['on', 'off', 'on', 'off']})


df['new'] = df.apply(lambda x: [np.array(x)], axis=1).apply(lambda x: x[0])

df

output:

    id  a       b       new
0   1   on      on      [1, on, on]
1   2   on      off     [2, on, off]
2   3   off     on      [3, off, on]
3   4   off     off     [4, off, off]

Upvotes: 0

Bharath M Shetty
Bharath M Shetty

Reputation: 30605

Use a wrapper around the numpy array i.e. pass the numpy array as list 

a = np.array([5, 6, 7, 8])
df = pd.DataFrame({"a": [a]})

Output:

             a
0  [5, 6, 7, 8]

Or you can use apply(np.array) by creating the tuples i.e. if you have a dataframe

df = pd.DataFrame({'id': [1, 2, 3, 4],
                   'a': ['on', 'on', 'off', 'off'],
                   'b': ['on', 'off', 'on', 'off']})

df['new'] = df.apply(lambda r: tuple(r), axis=1).apply(np.array)

Output :

     a    b  id            new
0   on   on   1    [on, on, 1]
1   on  off   2   [on, off, 2]
2  off   on   3   [off, on, 3]
3  off  off   4  [off, off, 4]

df['new'][0]

Output :

array(['on', 'on', '1'], dtype='<U2')

Upvotes: 74

user1717828
user1717828

Reputation: 7225

You can wrap the Data Frame data args in square brackets to maintain the np.array in each cell:

one_d_array = np.array([1,2,3])
two_d_array = one_d_array*one_d_array[:,np.newaxis]
two_d_array

array([[1, 2, 3],
       [2, 4, 6],
       [3, 6, 9]])


pd.DataFrame([
    [one_d_array],
    [two_d_array] ])

                                   0
0                          [1, 2, 3]
1  [[1, 2, 3], [2, 4, 6], [3, 6, 9]]

Upvotes: 6

Related Questions