问题描述
in[31]: day_1_variable.shape
out[31]: (241,241)
这是 241 * 241(行 * 列)的 10 个 numpy 数组的字典
df_dictionary = {'arrays_to_iterate': {'day_1': day_1_variable,'day_2': day_2_variable,'day_3': day_3_variable,.
.
.
.
.
.
'day_10': day_10_variable}}
day = 10
for days in np.arange(1,day+1):
numpy_array_to_iterate = df_dictionary ['arrays_to_iterate']['day_'+str(days)+'_rf']
variable_value_array=np.zeros((0),dtype='float') ## empty array of variable value created
for i in np.arange(numpy_array_to_iterate.shape[0]): ## iterating array rows
for j in np.arange(numpy_array_to_iterate.shape[1]): ## iterating array column
variable_value_at_specific_point=numpy_array_to_iterate[i][j]
variable_value_array=np.append(variable_value_array,variable_value_at_specific_point) ## values filled in array
df_xyz = pd.DataFrame()
for i in np.arange(1,day+1):
col_name = 'variable_day_' + str(i)
df_xyz.loc[:,col_name] = variable_value_array
df_xyz
我想将每一天的数组数据存储在 Pandas 数据框的列中,该列具有每个相应天的变量值
variable_day_1 variable_day_2 ........... variable_day_10
0 0.0625 0.0625 ........... 0.0625
1 0.0625 0.0625 ........... 0.0625
2 0.0625 0.0625 ........... 0.0625
3 0.0625 0.0625 ........... 0.0625
4 0.0625 0.0625 ........... 0.0625
... ... ... ... ... ... ... ... ... ... ...
58076 0.0000 0.0000 ........... 0.0000
58077 0.0000 0.0000 ........... 0.0000
58078 0.0000 0.0000 ........... 0.0000
58079 0.0000 0.0000 ........... 0.0000
58080 0.0000 0.0000 ........... 0.0000
58081 rows × 10 columns
怎么做?
解决方法
在字典值上使用 Numpy stack
(这将为您提供形状为 (10,241,241)
的 Numpy 数组)然后使用 reshape
将形状修改为 (10,58081)
,然后转置,将日期作为列放置。接下来,转换为 Pandas 数据框并使用字典键修复列名。
import pandas as pd
import numpy as np
#setup
np.random.seed(12345)
df_dictionary = {}
days = {f'day_{d}': np.random.rand(241,241).round(2) for d in range(1,11)}
df_dictionary['arrays_to_iterate'] = days
print(df_dictionary)
#code
all_days = np.stack(list(df_dictionary['arrays_to_iterate'].values())).reshape(10,-1).T
df = pd.DataFrame(all_days)
df.columns = df_dictionary['arrays_to_iterate'].keys()
print(df)
df_dictionary
的输出{'arrays_to_iterate':
{'day_1':
array(
[[0.93,0.32,0.18,...,0.62,0.89,0.78],[0.72,0.31,0.36,0.5,0.38],[0.36,0.77,0.03,0.57,0.04],[0.02,0.07,0.66,0.04]]),'day_2': array(
[[0.14,0.13,0.91,0.06,0.72,0.93],[0.13,0.02,0.09,0.39,0.13],...
来自df
的输出 day_1 day_2 day_3 day_4 day_5 day_6 day_7 day_8 day_9 day_10
0 0.93 0.14 0.06 0.10 0.01 0.66 0.67 0.18 0.93 0.40
1 0.32 0.13 0.81 0.57 0.23 0.60 0.48 0.07 0.08 0.32
2 0.18 0.91 0.95 0.27 0.36 0.11 0.25 0.71 0.24 0.44
3 0.20 0.51 0.52 0.62 0.09 0.31 0.19 0.78 0.83 0.58
4 0.57 0.14 0.89 0.51 0.67 0.29 0.48 0.95 0.36 0.97
... ... ... ... ... ... ... ... ... ... ...
58076 0.98 0.20 0.54 0.96 0.89 0.24 0.05 0.81 0.35 0.57
58077 0.53 0.96 0.04 0.60 0.16 0.38 0.83 0.49 0.28 0.02
58078 0.62 0.50 0.74 0.67 0.43 0.30 0.91 0.68 0.15 0.43
58079 0.50 0.11 0.57 0.42 0.85 0.97 0.86 0.60 0.75 0.33
58080 0.04 0.74 0.74 0.94 0.98 0.35 0.52 0.12 0.47 0.53
[58081 rows x 10 columns]