Python 3.x 我想分割数据并按行和列获取值

我希望通过将数据集拆分为80:20%的比率，将数据集与行和列一起拆分，其中80%是训练数据，20%是测试数据。但我可以将数据集分成80%，但不能分成20%

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

city_attributes = pd.read_csv('./input/city_attributes.csv')
humidity = pd.read_csv('./input/humidity.csv')
pressure = pd.read_csv('./input/pressure.csv')
temperature = pd.read_csv('./input/temperature.csv')
weather_description = pd.read_csv('./input/weather_description.csv')
wind_direction = pd.read_csv('./input/wind_direction.csv')
wind_speed = pd.read_csv('./input/wind_speed.csv')

# we can reshape these using pd.melt
humidity = pd.melt(humidity, id_vars = ['datetime'], value_name = 'humidity', var_name = 'City')
pressure = pd.melt(pressure, id_vars = ['datetime'], value_name = 'pressure', var_name = 'City')
temperature = pd.melt(temperature, id_vars = ['datetime'], value_name = 'temperature', var_name = 'City')
weather_description = pd.melt(weather_description, id_vars = ['datetime'], value_name = 'weather_description', var_name = 'City')
wind_direction = pd.melt(wind_direction, id_vars = ['datetime'], value_name = 'wind_direction', var_name = 'City')
wind_speed = pd.melt(wind_speed, id_vars = ['datetime'], value_name = 'wind_speed', var_name = 'City')

# combine all of the dataframes created above 
weather = pd.concat([humidity, pressure, temperature, wind_direction, wind_speed, weather_description], axis = 1)
weather = weather.loc[:,~weather.columns.duplicated()] # indexing: every row, only the columns that aren't duplicates

# now we can merge this with the city attributes
weather = pd.merge(city_attributes,weather, on = 'City')
weather = weather.dropna()

first = pd.DataFrame()
rest = pd.DataFrame()

total_size = weather.shape[0]
train_size = 1277055
test_size = 319264

if len(weather) > train_size: 
   first = weather[:1277055]
   rest = weather[319264:]

print(rest)

---

当前您的代码

train_size = 1277055
test_size = 319264

if len(weather) > train_size: 
    first = weather[:1277055]
    rest = weather[319264:]

将rest定义为第319264行之后的所有行，而第一行正确地定义为前1277055行。也许你想要的是

train_size = 1277055
test_size = 319264

if len(weather) > (train_size + test_size): 
    first = weather.iloc[:train_size, :] 
    rest = weather.iloc[(train_size + 1):(train_size + test_size + 1), :] # same as weather[1277056:1596320, :]

或者使用sklearn的列车测试分割：

train_size = 1277055
test_size = 319264
train_idx, test_idx = train_test_split(weather.index, train_size = train_size , test_size = test_size )

df_train = weather.iloc[train_idx, :]
df_test = weather.iloc[test_idx, :]

用法示例：

In [1]: import numpy as np
   ...: import pandas as pd
   ...: train_size = 1277055
   ...: test_size = 319264
   ...: weather = pd.DataFrame(np.random.randint(0,100,size=(train_size+test_size, 4)), columns=list('ABCD'))
   ...: print(weather.head())
    A   B   C   D
0  13  91  68  35
1  52  30  52  59
2  16  22  73  24
3  62  86  27  96
4  88  54  23   4

In [2]: if len(weather) >= (train_size + test_size):
   ...:     print('subsetting')
   ...:     first = weather.iloc[:train_size, :]
   ...:     rest = weather.iloc[(train_size + 1):(train_size + test_size + 1), :]
   ...: 
   ...:     print(first.shape)
   ...:     print(rest.shape)
   ...: 
subsetting
(1277055, 4)
(319263, 4)

---

要在位置x拆分数组，请使用

left  = array[:x]
right = array[x:]

---

与相同

x

。因为

x

是一个位置，而不是计数。

您会得到什么错误或意外结果？您导入了

train\u test\u split

，但未使用它。该函数应该完全满足您的需要。通过使用train_test_分割数据可以按列分割，而不是按行分割，我已经对其进行了测试。对于您提到的第一个结果，我得到了以下输出空数据帧列：[]索引：[]您能告诉我您使用的两个建议版本中的哪一个吗？另外，您是否在原始代码中插入了代码，替换了

if len（weather）.

块？我已经使用了这个块，是的，我已经在原始代码中插入了代码，可能需要首先进行一些更正=pd.DataFrame（）rest=pd.DataFrame（）if len（weather）>（训练大小+测试大小）：好，作为旁注，请记住'first=pd.DataFrame（）rest=pd.DataFrame（）`是不必要的，因为weather df的子集返回一个不同的df对象。您的天气df的总大小是多少？您确定天气df至少有（训练大小+测试大小）行吗？weather.shape的输出是多少？