Python 将 Numpy 数组按列转换为 Pandas DataFrame（作为单行）
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Convert Numpy array to Pandas DataFrame column-wise (As Single Row)
提问by Keithx
I have a numpy array looking like this:
我有一个看起来像这样的 numpy 数组：
a = np.array([35,2,160,56,120,80,1,1,0,0,1])
Then I'm trying to transform that array into pandas dataframe with logic "one column-one value" like this:
然后我试图将该数组转换为具有逻辑“一列一值”的熊猫数据框，如下所示：
columns=['age','gender','height',
'weight','ap_hi','ap_lo',
'cholesterol','gluc','smoke',
'alco','active']
values = a
df = pd.DataFrame(a,columns=columns)
This approach raises ValueError: Shape of passed values is (1, 11), indices imply (11, 11). What am I doing wrong and how to perform it in a right way?
这种方法会引发 ValueError：传递的值的形状是 (1, 11)，索引意味着 (11, 11)。我做错了什么以及如何以正确的方式执行它？
Thanks!
谢谢！
回答by jezrael
You need numpy.reshape:
你需要numpy.reshape：
columns=['age','gender','height',
'weight','ap_hi','ap_lo',
'cholesterol','gluc','smoke',
'alco','active']
a = np.array([35,2,160,56,120,80,1,1,0,0,1])
df = pd.DataFrame(a.reshape(-1, len(a)),columns=columns)
print (df)
age gender height weight ap_hi ap_lo cholesterol gluc smoke alco \
0 35 2 160 56 120 80 1 1 0 0
active
0 1
If the reshape operation is not clear to read, a more explicit way of adding a dimension to the 1d array is to use numpy.atleast_2d
如果 reshape 操作不清楚读取，则向一维数组添加维度的更明确方法是使用 numpy.atleast_2d
pd.DataFrame(np.atleast_2d(a), columns=columns)
Or simplier add [](but slower if really many columns):
或者更简单地添加[]（但如果真的很多列会更慢）：
df = pd.DataFrame([a],columns=columns)
print (df)
age gender height weight ap_hi ap_lo cholesterol gluc smoke alco \
0 35 2 160 56 120 80 1 1 0 0
active
0 1
Thanks Divakar for suggestion:
感谢 Divakar 的建议：
df = pd.DataFrame(a[None],columns=columns)
print (df)
age gender height weight ap_hi ap_lo cholesterol gluc smoke alco \
0 35 2 160 56 120 80 1 1 0 0
active
0 1
And another solution, thanks piRSquared:
另一个解决方案，感谢piRSquared：
pd.DataFrame([a], [0], columns)
回答by Alex F
Just reshape the array to what you need for the dataframe.
只需将数组重塑为数据框所需的内容。
import pandas as pd
import numpy as np
a = np.array([35,2,160,56,120,80,1,1,0,0,1])
columns=['age','gender','height',
'weight','ap_hi','ap_lo',
'cholesterol','gluc','smoke',
'alco','active']
df = pd.DataFrame(np.reshape(a, (1,len(a))),columns=columns)
