英文:
How do I append variables to a dataframe coming from a function passed in a Python loop?
问题
Here is the translated code section you provided:
我的目标是获得这个输出：
campaign | mean | left | right
A | 5% | 0% | 10%
B | 8% | 2% | 6% etc.
目前，我正在使用一个循环内的Python函数来获取这4个变量：
campaign、mean、left、right。以下是该函数：
```python
import numpy as np
def bootstrap_ci(df, variable, classes, repetitions = 1000, alpha = 0.05, random_state=None):
df = df[[variable, classes]]
bootstrap_sample_size = len(df)
mean_diffs = []
for i in range(repetitions):
bootstrap_sample = df.sample(n = bootstrap_sample_size, replace = True, random_state = random_state)
mean_diff = bootstrap_sample.groupby(classes).mean().iloc[1,0] - bootstrap_sample.groupby(classes).mean().iloc[0,0]
mean_diffs.append(mean_diff)
left = np.percentile(mean_diffs, alpha/2*100)*(-1)
right = np.percentile(mean_diffs, 100-alpha/2*100)*(-1)
mean = -(df.groupby(classes).mean().iloc[1,0] - df.groupby(classes).mean().iloc[0,0])
接下来，我正在创建一个循环来运行所有我的广告系列的自举函数（这个工作）。
for i in campaigns:
print(Lift for: {i}')
bootstrap_ci(df[df['campaign_name']==i],'conversion','group')
然后，我尝试将以下代码添加到循环函数中，以将left、right、mean这些变量附加到df（这个不起作用）：
df = pd.DataFrame([]) --初始化一个新的df
for i in campaigns:
print(Lift for: {i}')
bootstrap_ci(df[df['campaign_name']==i],'conversion','group')
df = df.append(i, mean, left, right) --将相关变量附加到df
请注意，你在附加变量时需要将它们包装在字典或Series中，以确保正确附加到DataFrame中。你可以使用类似以下的代码：
df = pd.DataFrame([]) # 初始化一个新的df
for i in campaigns:
print(f'Lift for: {i}')
result = bootstrap_ci(df[df['campaign_name']==i],'conversion','group')
df = df.append({'campaign': i, 'mean': result['mean'], 'left': result['left'], 'right': result['right']}, ignore_index=True)
这样，你将能够将每个广告系列的结果添加到DataFrame中。
英文:
My goal is to have this output:
campaign | mean | left | right
A | 5% | 0% | 10%
B | 8% | 2% | 6% etc.
Right now, I am using a python function within a loop to get the 4 variables:
campaign, mean, left, right. Here is the function:
import numpy as np
def bootstrap_ci(df, variable, classes, repetitions = 1000, alpha = 0.05, random_state=None):df = df[[variable, classes]]bootstrap_sample_size = len(df)
mean_diffs = []
for i in range(repetitions):
bootstrap_sample = df.sample(n = bootstrap_sample_size, replace = True, random_state = random_state)
mean_diff = bootstrap_sample.groupby(classes).mean().iloc[1,0] - bootstrap_sample.groupby(classes).mean().iloc[0,0]
mean_diffs.append(mean_diff)
left = np.percentile(mean_diffs, alpha/2*100)*(-1)
right = np.percentile(mean_diffs, 100-alpha/2*100)*(-1)
mean = -(df.groupby(classes).mean().iloc[1,0] - df.groupby(classes).mean().iloc[0,0])
Next, I am creating a loop to run the bootstrap function for all my campaigns (this works).
for i in campaigns:
print(Lift for: {i}')
bootstrap_ci(df[df['campaign_name']==i],'conversion','group')
Then, I tried adding this code to the loop function to append the variables left, right, mean (this does not work):
df = pd.DataFrame([]) --initiate a new df
for i in campaigns:
print(Lift for: {i}')
bootstrap_ci(df[df['campaign_name']==i],'conversion','group')
df = df.append(i, mean, left, right) --append the relevant variables to df
Please advise as the df returns nothing...
答案1
得分: 0
Append new items with df.at[index, column] = value, as follows:
import numpy as np
import pandas as pd
def bootstrap_ci(df, variable, classes, repetitions=1000, alpha=0.05, random_state=None):
df = df[[variable, classes]]
bootstrap_sample_size = len(df)
mean_diffs = []
for i in range(repetitions):
bootstrap_sample = df.sample(n=bootstrap_sample_size, replace=True,
random_state=random_state)
mean_diff = bootstrap_sample.groupby(classes).mean().iloc[1, 0] - bootstrap_sample.groupby(classes).mean().iloc[0, 0]
mean_diffs.append(mean_diff)
left = np.percentile(mean_diffs, alpha / 2 * 100) * (-1)
right = np.percentile(mean_diffs, 100 - alpha / 2 * 100) * (-1)
mean = -(df.groupby(classes).mean().iloc[1, 0] - df.groupby(classes).mean().iloc[0, 0])
# added return statement
return dict(mean=mean, left=left, right=right)
# initiate a new df
new_df = pd.DataFrame({'mean': [], 'left': [], 'right': []})
for i in campaigns:
print('Lift for: {i}')
values = bootstrap_ci(df[df['campaign_name'] == i], 'conversion', 'group')
# append the relevant variables to df
for k, v in values.items():
new_df.at[i, k] = v
英文:
Append new items with df.at[index, column] = value, as follows:
import numpy as np
import pandas as pd
def bootstrap_ci(df, variable, classes, repetitions=1000, alpha=0.05, random_state=None):
df = df[[variable, classes]]
bootstrap_sample_size = len(df)
mean_diffs = []
for i in range(repetitions):
bootstrap_sample = df.sample(n=bootstrap_sample_size, replace=True,
random_state=random_state)
mean_diff = bootstrap_sample.groupby(classes).mean().iloc[1, 0] - bootstrap_sample.groupby(classes).mean().iloc[0, 0]
mean_diffs.append(mean_diff)
left = np.percentile(mean_diffs, alpha / 2 * 100) * (-1)
right = np.percentile(mean_diffs, 100 - alpha / 2 * 100) * (-1)
mean = -(df.groupby(classes).mean().iloc[1, 0] - df.groupby(classes).mean().iloc[0, 0])
# added return statement
return dict(mean=mean, left=left, right=right)
# initiate a new df
new_df = pd.DataFrame({'mean': [], 'left': [], 'right': []})
for i in campaigns:
print('Lift for: {i}')
values = bootstrap_ci(df[df['campaign_name'] == i], 'conversion', 'group')
# append the relevant variables to df
for k, v in values.items():
new_df.at[i, k] = v
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