pandas

问题 I have a dataframe that looks like in the pictures. Now, I want to add a new column that will show the average of power for each day (given the data is sampled every 5 minutes), but separately for when it is day_or_night (day = 0 in the column, night = 1). I've gotten this far: train['avg_by_day'][train['day_or_night']==1] = train['power'][train['day_or_night']==1].mean() train['avg_by_day'][train['day_or_night']==0] = train['power'][train['day_or_night']==0].mean() but this just adds the

问题 I would like to sum the frequencies over multiple columns with pandas. The amount of columns can vary between 2-15 columns. Here is an example of just 3 columns: code1 code2 code3 27 5 56 534 27 78 27 312 55 89 312 27 And I would like to have the following result: code frequency 5 1 27 4 55 1 56 2 78 1 312 2 534 1 To count values inside one column is not the problem, just need a sum of all frequencies in a dataframe a value can appear, no matter the amount of columns. 回答1: You could stack and

问题 I have a dataframe that looks like in the pictures. Now, I want to add a new column that will show the average of power for each day (given the data is sampled every 5 minutes), but separately for when it is day_or_night (day = 0 in the column, night = 1). I've gotten this far: train['avg_by_day'][train['day_or_night']==1] = train['power'][train['day_or_night']==1].mean() train['avg_by_day'][train['day_or_night']==0] = train['power'][train['day_or_night']==0].mean() but this just adds the

问题 I have following dataframe in pandas C1 C2 C3 10 a b 10 a b ? c c ? ? b 10 a b 10 ? ? I want to count the occurrences of ? in all the columns My desired output is column wise sum of occurrences 回答1: Use: m=df.eq('?').sum() pd.DataFrame([m.values],columns=m.index) C1 C2 C3 0 2 2 1 Or better : df.eq('?').sum().to_frame().T #thanks @user3483203 C1 C2 C3 0 2 2 1 来源： https://stackoverflow.com/questions/54714183/count-occurrences-of-certain-string-in-entire-pandas-dataframe

问题 What I'm trying to do is a kind of a cross sell. I have a Pandas dataframe with two columns, one with receipt numbers, and the other with product ids: receipt product 1 a 1 b 2 c 3 b 3 a Most of the receipts have many products. What I need to find is the count of combinations of products that happen in the receipts. Let's say products 'a' and 'b' are the most common combination (they appear together in most of the receipts), how do I find this information? I tried using df.groupby(['receipt',

问题 I have a dataframe that I have grouped by multiple columns. Within each group, I would like to then generate a value that finds the last entity of each of those groups and divide by the first entity. I would also like to show the number of entities and the last entity value in the output. See below for an example data and the desired output. I know how to show the count of the group, shown below in the code. df_group=df.groupby(['ID','Item','End_Date','Type']) df_output=df_group.size().reset

问题 I have an excel dataset containing datetime values of worked hours entered by employees. Now that the end of the year is near they want to report on it, however it is full of wrong entries. Thus I need to clean it. Herebelow some examples of wrong entries. What would be your approach when facing such datasets? I first converted date column to datetime using df['Shiftdatum'] = pd.to_datetime(df.Shiftdatum, format='%Y-%m-%d', errors='coerce') In below's sampledata it shows a NaT How do I filter

问题 Let's say I have the following dataframe series df['Name'] column: Name 'Jerry' 'Adam (and family)' 'Paul and Hellen (and family):\n' 'John and Peter (and family):/n' How would I remove all the contents in Name after the first parentheses? df['Name']= df['Name'].str.split("'(").str[0] doesn't seem to work and I don't understand why? The output I want is Name 'Jerry' 'Adam' 'Paul and Hellen' 'John and Peter' so everything after the parentheses is deleted. 回答1: Solution with split - is

问题 I have a dataframe as follows: A B zDate 01-JAN-17 100 200 02-JAN-17 111 203 03-JAN-17 NaN 202 04-JAN-17 109 205 05-JAN-17 101 211 06-JAN-17 105 NaN 07-JAN-17 104 NaN What is the best way, to fill the missing values, using last available ones? Following is the intended result: A B zDate 01-JAN-17 100 200 02-JAN-17 111 203 03-JAN-17 111 202 04-JAN-17 109 205 05-JAN-17 101 211 06-JAN-17 105 211 07-JAN-17 104 211 回答1: Use ffill function, what is same as fillna with method ffill : df = df.ffill()

问题 I am trying to create some sort of "functional select" that gives users flexibility to create configuration to select data in pandas dataframes. However I ran into some issues that puzzle me. The following is a simplified example: >>> import pandas as pd >>> df = pd.DataFrame({'date': pd.date_range(start='2020-01-01', periods=4), 'val': [1, 2, 3, 4]}) >>> df date val 0 2020-01-01 1 1 2020-01-02 2 2 2020-01-03 3 3 2020-01-04 4 Question 1: Why do I get different result when I apply the function