pandas 基于列值的随机抽样熊猫
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Random sampling pandas based on column values
提问by RnK
I have files (A,B,C etc) each having 12,000 data points. I have divided the files into batches of 1000 points and computed the value for each batch. So now for each file we have 12 values, which is loaded in a pandas Data Frame (shown below).
我有文件（A、B、C 等），每个文件都有 12,000 个数据点。我已将文件分成 1000 个点的批次并计算每个批次的值。所以现在对于每个文件，我们有 12 个值，它们被加载到一个 Pandas 数据帧中（如下所示）。
file value_1 value_2
0 A 1 43
1 A 1 89
2 A 1 22
3 A 1 87
4 A 1 43
5 A 1 89
6 A 1 22
7 A 1 87
8 A 1 43
9 A 1 89
10 A 1 22
11 A 1 87
12 A 1 83
13 B 0 99
14 B 0 23
15 B 0 29
16 B 0 34
17 B 0 99
18 B 0 23
19 B 0 29
20 B 0 34
21 B 0 99
22 B 0 23
23 B 0 29
24 B 0 34
25 C 1 62
- - - -
- - - -
Now as the next step I need to randomly select a file, and for that file randomly select a sequence of 4 batches for value_1. The later, I believe can be done with df.sample(), but I'm not sure how to randomly select the files. I tried to make it work with np.random.choice(data['file'].unique()), but doesn't seems correct.
现在作为下一步，我需要随机选择一个文件，并为该文件随机为 value_1 选择 4 个批次的序列。后者，我相信可以用 df.sample() 来完成，但我不确定如何随机选择文件。我试图让它与 np.random.choice(data['file'].unique()) 一起工作，但似乎不正确。
Thanks for the help in advance. I'm pretty new to pandas and python in general.
我在这里先向您的帮助表示感谢。总的来说，我对Pandas和蟒蛇很陌生。
回答by Abdou
If I understand what you are trying to get at, the following should be of help:
如果我理解您想要达到的目的，以下内容应该会有所帮助：
# Test dataframe
import numpy as np
import pandas as pd
data = pd.DataFrame({'file': np.repeat(['A', 'B', 'C'], 12),
'value_1': np.repeat([1,0,1],12),
'value_2': np.random.randint(20, 100, 36)})
# Select a file
data1 = data[data.file == np.random.choice(data['file'].unique())].reset_index(drop=True)
# Get a random index from data1
start_ix = np.random.choice(data1.index[:-3])
# Get a sequence starting at the random index from the previous step
print(data.loc[start_ix:start_ix+3])
回答by Jason
Here's a rather long winded answer that has a lot of flexibility and uses some random data I generated. I also added a field to the dataframeto denote whether that row had been used.
这是一个相当冗长的答案，它具有很大的灵活性并使用我生成的一些随机数据。我还在 中添加了一个字段来dataframe表示该行是否已被使用。
Generating Data
生成数据
import pandas as pd
from string import ascii_lowercase
import random
random.seed(44)
files = [ascii_lowercase[i] for i in range(4)]
value_1 = random.sample(range(1, 10), 8)
files_df = files*len(value_1)
value_1_df = value_1*len(files)
value_1_df.sort()
value_2_df = random.sample(range(100, 200), len(files_df))
df = pd.DataFrame({'file' : files_df,
'value_1': value_1_df,
'value_2': value_2_df,
'used': 0})
Randomly Selecting Files
随机选择文件
len_to_run = 3 #change to run for however long you'd like
batch_to_pull = 4
updated_files = df.loc[df.used==0,'file'].unique()
for i in range(len_to_run): #not needed if you only want to run once
file_to_pull = ''.join(random.sample(updated_files, 1))
print 'file ' + file_to_pull
for j in range(batch_to_pull): #pulling 4 values
updated_value_1 = df.loc[(df.used==0) & (df.file==file_to_pull),'value_1'].unique()
value_1_to_pull = random.sample(updated_value_1,1)
print 'value_1 ' + str(value_1_to_pull)
df.loc[(df.file == file_to_pull) & (df.value_1==value_1_to_pull),'used']=1
file a
value_1 [1]
value_1 [7]
value_1 [5]
value_1 [4]
file d
value_1 [3]
value_1 [2]
value_1 [1]
value_1 [5]
file d
value_1 [7]
value_1 [4]
value_1 [6]
value_1 [9]
