I have a Pandas data frame object of shape (X,Y) that looks like this:
[[1, 2, 3],
[4, 5, 6],
[7, 8, 9]]
and a numpy sparse matrix (CSC) of shape (X,Z) that looks something like this
[[0, 1, 0],
[0, 0, 1],
[1, 0, 0]]
How can I add the content from the matrix to the data frame in a new named column such that the data frame will end up like this:
[[1, 2, 3, [0, 1, 0]],
[4, 5, 6, [0, 0, 1]],
[7, 8, 9, [1, 0, 0]]]
Notice the data frame now has shape (X, Y+1) and rows from the matrix are elements in the data frame.
import numpy as np
import pandas as pd
import scipy.sparse as sparse
df = pd.DataFrame(np.arange(1,10).reshape(3,3))
arr = sparse.coo_matrix(([1,1,1], ([0,1,2], [1,2,0])), shape=(3,3))
df['newcol'] = arr.toarray().tolist()
print(df)
yields
0 1 2 newcol
0 1 2 3 [0, 1, 0]
1 4 5 6 [0, 0, 1]
2 7 8 9 [1, 0, 0]
Here is other example:
import numpy as np
import pandas as pd
""" This just creates a list of touples, and each element of the touple is an array"""
a = [ (np.random.randint(1,10,10), np.array([0,1,2,3,4,5,6,7,8,9])) for i in
range(0,10) ]
""" Panda DataFrame will allocate each of the arrays , contained as a touple
element , as column"""
df = pd.DataFrame(data =a,columns=['random_num','sequential_num'])
The secret in general is to allocate the data in the form a = [ (array_11, array_12,...,array_1n),...,(array_m1,array_m2,...,array_mn) ] and panda DataFrame will order the data in n columns of arrays. Of course , arrays of arrays could be used instead of touples, in that case the form would be : a = [ [array_11, array_12,...,array_1n],...,[array_m1,array_m2,...,array_mn] ]
This is the output if you print(df) from the code above:
random_num sequential_num
0 [7, 9, 2, 2, 5, 3, 5, 3, 1, 4] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
1 [8, 7, 9, 8, 1, 2, 2, 6, 6, 3] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
2 [3, 4, 1, 2, 2, 1, 4, 2, 6, 1] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
3 [3, 1, 1, 1, 6, 2, 8, 6, 7, 9] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
4 [4, 2, 8, 5, 4, 1, 2, 2, 3, 3] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
5 [3, 2, 7, 4, 1, 5, 1, 4, 6, 3] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
6 [5, 7, 3, 9, 7, 8, 4, 1, 3, 1] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
7 [7, 4, 7, 6, 2, 6, 3, 2, 5, 6] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
8 [3, 1, 6, 3, 2, 1, 5, 2, 2, 9] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
9 [7, 2, 3, 9, 5, 5, 8, 6, 9, 8] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
Other variation of the example above:
b = [ (i,"text",[14, 5,], np.array([0,1,2,3,4,5,6,7,8,9])) for i in
range(0,10) ]
df = pd.DataFrame(data=b,columns=['Number','Text','2Elemnt_array','10Element_array'])
Output of df:
Number Text 2Elemnt_array 10Element_array
0 0 text [14, 5] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
1 1 text [14, 5] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
2 2 text [14, 5] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
3 3 text [14, 5] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
4 4 text [14, 5] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
5 5 text [14, 5] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
6 6 text [14, 5] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
7 7 text [14, 5] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
8 8 text [14, 5] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
9 9 text [14, 5] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
If you want to add other columns of arrays, then:
df['3Element_array']=[([1,2,3]),([1,2,3]),([1,2,3]),([1,2,3]),([1,2,3]),([1,2,3]),([1,2,3]),([1,2,3]),([1,2,3]),([1,2,3])]
The final output of df will be:
Number Text 2Elemnt_array 10Element_array 3Element_array
0 0 text [14, 5] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] [1, 2, 3]
1 1 text [14, 5] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] [1, 2, 3]
2 2 text [14, 5] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] [1, 2, 3]
3 3 text [14, 5] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] [1, 2, 3]
4 4 text [14, 5] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] [1, 2, 3]
5 5 text [14, 5] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] [1, 2, 3]
6 6 text [14, 5] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] [1, 2, 3]
7 7 text [14, 5] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] [1, 2, 3]
8 8 text [14, 5] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] [1, 2, 3]
9 9 text [14, 5] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] [1, 2, 3]
You can add and retrieve a numpy array from dataframe using this:
import numpy as np
import pandas as pd
df = pd.DataFrame({'b':range(10)}) # target dataframe
a = np.random.normal(size=(10,2)) # numpy array
df['a']=a.tolist() # save array
np.array(df['a'].tolist()) # retrieve array
This builds on the previous answer that confused me because of the sparse part and this works well for a non-sparse numpy arrray.
df = pd.DataFrame(np.arange(1,10).reshape(3,3))
df['newcol'] = pd.Series(your_2d_numpy_array)
Consider using a higher dimensional datastructure (a Panel), rather than storing an array in your column:
In [11]: p = pd.Panel({'df': df, 'csc': csc})
In [12]: p.df
Out[12]:
0 1 2
0 1 2 3
1 4 5 6
2 7 8 9
In [13]: p.csc
Out[13]:
0 1 2
0 0 1 0
1 0 0 1
2 1 0 0
Look at cross-sections etc, etc, etc.
In [14]: p.xs(0)
Out[14]:
csc df
0 0 1
1 1 2
2 0 3
Source: Stackoverflow.com