I want to get a 2d-numpy array from a column of a pandas dataframe df having a numpy vector in each row. But if I do
df.values.shape
I get: (3,) instead of getting: (3,5)
(assuming that each numpy vector in the dataframe has 5 dimensions, and that the dataframe has 3 rows)
what is the correct method?
Ideally, avoid getting into this situation by finding a different way to define the DataFrame in the first place. However, if your DataFrame looks like this:
s = pd.Series([np.random.randint(20, size=(5,)) for i in range(3)])
df = pd.DataFrame(s, columns=['foo'])
# foo
# 0 [4, 14, 9, 16, 5]
# 1 [16, 16, 5, 4, 19]
# 2 [7, 10, 15, 13, 2]
then you could convert it to a DataFrame of shape (3,5) by calling pd.DataFrame on a list of arrays:
pd.DataFrame(df['foo'].tolist())
# 0 1 2 3 4
# 0 4 14 9 16 5
# 1 16 16 5 4 19
# 2 7 10 15 13 2
pd.DataFrame(df['foo'].tolist()).values.shape
# (3, 5)
I am not sure what you want. But df.values.shape seems to be giving the correct result.
import pandas as pd
import numpy as np
from pandas import DataFrame
df3 = DataFrame(np.random.randn(3, 5), columns=['a', 'b', 'c', 'd', 'e'])
print df3
# a b c d e
#0 -0.221059 1.206064 -1.359214 0.674061 0.547711
#1 0.246188 0.628944 0.528552 0.179939 -0.019213
#2 0.080049 0.579549 1.790376 -1.301700 1.372702
df3.values.shape
#(3L, 5L)
df3["a"]
#0 -0.221059
#1 0.246188
#2 0.080049
df3[:1]
# a b c d e
#0 -0.221059 1.206064 -1.359214 0.674061 0.547711
