英文:
Can the .loc command be used with groupby's apply function
问题
以下是您提供的代码部分的翻译：
data = {'id': ['205', '205', '204', '204', '204'],'Sentiment': ['Positive', 'Positive', 'Neutral', 'Positive', 'Positive']}df = pd.DataFrame(data)df['freq'] = df.groupby('Sentiment')['id'].transform(pd.Series.nunique)df['freq_sum'] = df.groupby('id')['freq'].transform(pd.Series.count)df['freq_cent'] = (df['freq'] / df['freq_sum'])df['sent_inclination'] = df.loc[df['freq_cent'] > 0.5, ['Sentiment']]
请注意，这是代码的翻译部分。如果您有任何其他问题或需要进一步的帮助，请随时提出。
英文:
This question already has answers here:
Pandas conditional creation of a series/dataframe column (13 answers)
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Hi. I have a DataFrame with multiple columns where I have successfully assigned values in a new column (sent_inclination) given a condition. However, I want to change the output such that the values are given to the entire given group identified by the column "id". The condition is: if "freq_cent" > 0.5, then the new column should have the value from the column 'Sentiment' in it for the given id. That is whether the freq_cent is greater than 0.5 or less than 0.5 for a given observation, the new column should contain the sentiment value that qualifies for greater than 0.5 for that entire group.
I am able to assign values to the entire DataFrame but cannot make sure that the values are the same for the entire group.
Here is my sample DataFrame:
data = {'id': ['205', '205', '204', '204', '204'],'Sentiment': ['Positive', 'Positive', 'Neutral', 'Positive', 'Positive']}df = pd.DataFrame(data)df['freq'] = df.groupby('Sentiment')['id'].transform(pd.Series.nunique)df['freq_sum'] = df.groupby('id')['freq'].transform(pd.Series.count)df['freq_cent'] = (df['freq']/df['freq_sum'])
where if I apply the code:
df['sent_inclination'] = df.loc[df['freq_cent'] >0.5, ['Sentiment']]
I get the output:
id Sentiment freq freq_sum freq_cent sent_inclination0 205 Positive 2 2 1.000000 Positive1 205 Positive 2 2 1.000000 Positive2 204 Neutral 1 3 0.333333 NaN3 204 Positive 2 3 0.666667 Positive4 204 Positive 2 3 0.666667 Positive
The desired output should have 'sent_inclination' as Positive for all observations where id is 204, that is:
id Sentiment freq freq_sum freq_cent sent_inclination0 205 Positive 2 2 1.000000 Positive1 205 Positive 2 2 1.000000 Positive2 204 Neutral 1 3 0.333333 Positive3 204 Positive 2 3 0.666667 Positive4 204 Positive 2 3 0.666667 Positive
How can I achieve this? Any suggestions will be highly appreciated. Unfortunately the groupby.filter method doesn't work for me.
So far I have tried multiple codes, some of which are as follows:
df['sent_inclination'] = df.loc[df.groupby('id').apply(lambda x: df.loc[df['freq_cent'] >0.5, df['Sentiment']])]df['sent_inclination'] = df.groupby('id').apply(lambda x: (df.query('freq_cent >0.5')['Sentiment']))df.groupby('id').apply(lambda x: x['sent_inclination'] == x['Sentiment'] if (x['freq_cent'] > 0.5) else '')df.groupby('id').apply(lambda x: x['sent_inclination'] == (df.query('freq_cent >0.5')['Sentiment']))
答案1
得分: 1
I recommend to use groupby from pandas and where from numpy:
import pandas as pdimport numpy as np# this will get you an appended dataframe where the maximum per group is picked (you can also use "mean" instead of "max" to get the group average)df = pd.merge(df, df.groupby(['id'])['freq_cent'].max().reset_index(), on='id', how='left')# this will check if the value is greater than 0.5df['sent_inclination'] = np.where(df['freq_cent_y'] > 0.5, 'Positive', df['Sentiment'])# cleaning and renamingdf.rename(columns={"freq_cent_x": "freq_cent_x"}, inplace=True)df = df[['id', 'freq', 'freq_sum', 'freq_cent_x', 'sent_inclination']]
Output:
print(df)id freq freq_sum freq_cent_x sent_inclination0 205 2 2 1.000000 Positive1 205 2 2 1.000000 Positive2 204 1 3 0.333333 Positive3 204 2 3 0.666667 Positive4 204 2 3 0.666667 Positive
Based on the condition, you can also adjust it; simply change the line with np.where:
df['sent_inclination'] = np.where(df['freq_cent_y'] > 0.5, 'Positive', np.where(df['freq_cent_y'] < 0.33, 'Negative', 'Neutral'))
This would give an outcome where >0.5 is "Positive," between 0.5 and 0.33 is "Neutral," and <0.33 is "Negative."
英文:
I recommend to use groupbyfrom pandas und wherefrom numpy:
import pandas as pdimport numpy as np#this will get you a appended dataframe where the maximum per group is picked (you can also use "mean" instead of "max" to get the group averagedf = pd.merge(df, df.groupby(['id'])['freq_cent'].max().reset_index(), on='id', how='left')#this will check the value is greater then 0,5df['sent_inclination'] = np.where(df['freq_cent_y'] >0.5, 'Positive', df['Sentiment'])#cleaning and renamedf.rename(columns={"freq_cent_x": "freq_cent_x"}, inplace=True)df = df[['id', 'freq', 'freq_sum', 'freq_cent_x', 'sent_inclination']]
output:
print(df)id freq freq_sum freq_cent_x sent_inclination0 205 2 2 1.000000 Positive1 205 2 2 1.000000 Positive2 204 1 3 0.333333 Positive3 204 2 3 0.666667 Positive4 204 2 3 0.666667 Positive
Based on the condition you can also adjust it, simple change the line with np.where:
df['sent_inclination'] = np.where(df['freq_cent_y'] >0.5, 'Positive', np.where(df['freq_cent_y' <0.33, 'Negative', 'Neutral'))
would give a outcome where >0.5 is "Positive", between 0.5 and 0.33 "Neutral", and <0.33 "Negative"
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