I would like to create automatically in Python n of numpy arrays from my pandas dataframe columns. I can do this manually using for example:
numpy_array_1 = data_frame.column_1.values
numpy_array_2 = data_frame.column_2.values
...
numpy_array_n = data_frame.column_n.values
But I can not know how I should write code to create those arrays automatically.
You can simply use a for and loop through it. Remember that using (list(data_frame)) returns a list of the column names in the dataframe:
np_array = []
for i in list(data_frame):
np_array.append(data_frame[i].values)
The expected output is a list that contains sublists of values. Where each sublist matches the position of the columns in the dataframe. Therefore you can either make a dictionary, or a tuple out of it. Dictionary example:
np_array_dict = {}
for i in list(data_frame):
np_array_dict[i] = data_frame[i].values
You can get matrix of all dataframe rows and columns values as simple as df.values Do you really need distinct array for each column?
Suppose we have a simple df:
df = pd.DataFrame({"0":[1,2], "1":[3,4]})
df
0 1
0 1 3
1 2 4
Then you can run:
for (key,value) in df.to_dict("list").items():
exec("numpy_array_{} = np.array({})".format(key, value))
You'll get:
numpy_array_0
array([1, 2])
numpy_array_1
array([3, 4])
and so on.
Alternatively:
for col in list(df):
exec("numpy_array_{} = df[str({})].values".format(col,col))
This can be done without using loops:
df = pd.DataFrame({"0":[1,2], "1":[3,4], "2":[5,6]})
print(df)
0 1 2
0 1 3 5
1 2 4 6
and then:
[*np.transpose(df.values)]
results in:
[array([1, 2]), array([3, 4]), array([5, 6])]
and if a dictionary is desired one just needs to proceed as follows:
dict(zip(range(df.shape[1]), [*np.transpose(df.values)]))
which gives:
{0: array([1, 2]), 1: array([3, 4]), 2: array([5, 6])}
