使用包含多種類型的numpy數組創建一個Pandas DataFrame

Question

我想創建一個默認值爲零，但是一列整數和另一個浮點數的熊貓數據框。我可以用正確的類型創建一個numpy數組，請參閱下面的values變量。但是，當我將它傳遞給數據框構造函數時，它只返回NaN值（請參閱下面的df）。我有包括返回float數組無類型代碼（見df2）

import pandas as pd 
import numpy as np 

values = np.zeros((2,3), dtype='int32,float32') 
index = ['x', 'y'] 
columns = ['a','b','c'] 

df = pd.DataFrame(data=values, index=index, columns=columns) 
df.values.dtype 

values2 = np.zeros((2,3)) 
df2 = pd.DataFrame(data=values2, index=index, columns=columns) 
df2.values.dtype 

如何構建數據框有什麼建議？

Answer 1

這裏有幾個選項，你可以選擇：以上

import numpy as np 
import pandas as pd 

index = ['x', 'y'] 
columns = ['a','b','c'] 

# Option 1: Set the column names in the structured array's dtype 
dtype = [('a','int32'), ('b','float32'), ('c','float32')] 
values = np.zeros(2, dtype=dtype) 
df = pd.DataFrame(values, index=index) 

# Option 2: Alter the structured array's column names after it has been created 
values = np.zeros(2, dtype='int32, float32, float32') 
values.dtype.names = columns 
df2 = pd.DataFrame(values, index=index, columns=columns) 

# Option 3: Alter the DataFrame's column names after it has been created 
values = np.zeros(2, dtype='int32, float32, float32') 
df3 = pd.DataFrame(values, index=index) 
df3.columns = columns 

# Option 4: Use a dict of arrays, each of the right dtype: 
df4 = pd.DataFrame(
    {'a': np.zeros(2, dtype='int32'), 
    'b': np.zeros(2, dtype='float32'), 
    'c': np.zeros(2, dtype='float32')}, index=index, columns=columns) 

# Option 5: Concatenate DataFrames of the simple dtypes: 
df5 = pd.concat([ 
    pd.DataFrame(np.zeros((2,), dtype='int32'), columns=['a']), 
    pd.DataFrame(np.zeros((2,2), dtype='float32'), columns=['b','c'])], axis=1) 

# Option 6: Alter the dtypes after the DataFrame has been formed. (This is not very efficient) 
values2 = np.zeros((2, 3)) 
df6 = pd.DataFrame(values2, index=index, columns=columns) 
for col, dtype in zip(df6.columns, 'int32 float32 float32'.split()): 
    df6[col] = df6[col].astype(dtype) 

每個選項產生相同的結果

a b c 
x 0 0 0 
y 0 0 0 

與dtypes：

a  int32 
b float32 
c float32 
dtype: object 

---

爲什麼pd.DataFrame(values, index=index, columns=columns)生產用的NaN一個數據幀：

values是一個結構數組列名f0，f1，f2：

In [171]: values 
Out[172]: 
array([(0, 0.0, 0.0), (0, 0.0, 0.0)], 
     dtype=[('f0', '<i4'), ('f1', '<f4'), ('f2', '<f4')]) 

如果您傳遞參數columns=['a', 'b', 'c']到pd.DataFrame，那麼熊貓會尋找與這些列結構化數組values中的名稱。當找不到那些列時，Pandas會在DataFrame中放置NaN以表示缺失值。