pandas

问题 This question already has answers here : Calculate Pandas DataFrame Time Difference Between Two Columns in Hours and Minutes (3 answers) Closed 2 years ago . My df looks like, start stop 0 2015-11-04 10:12:00 2015-11-06 06:38:00 1 2015-11-04 10:23:00 2015-11-05 08:30:00 2 2015-11-04 14:01:00 2015-11-17 10:34:00 4 2015-11-19 01:43:00 2015-12-21 09:04:00 print(time_df.dtypes) start datetime64[ns] stop datetime64[ns] dtype: object I am trying to find the time difference between, stop and start.

问题 This question already has answers here : Calculate Pandas DataFrame Time Difference Between Two Columns in Hours and Minutes (3 answers) Closed 2 years ago . My df looks like, start stop 0 2015-11-04 10:12:00 2015-11-06 06:38:00 1 2015-11-04 10:23:00 2015-11-05 08:30:00 2 2015-11-04 14:01:00 2015-11-17 10:34:00 4 2015-11-19 01:43:00 2015-12-21 09:04:00 print(time_df.dtypes) start datetime64[ns] stop datetime64[ns] dtype: object I am trying to find the time difference between, stop and start.

问题 I have two dataframes: >>> df1 [Output]: col1 col2 col3 col4 a abc 10 str1 b abc 20 str2 c def 20 str2 d abc 30 str2 >>> df2 [Output]: col1 col2 col3 col5 col6 d abc 30 str6 47 b abc 20 str5 66 c def 20 str7 53 a abc 10 str5 21 Below is what I want to generate: >>> df_merged [Output]: col1 col2 col5 a abc str5 b abc str5 c def str7 d abc str6 I don't want to generate more than 4 rows and that is usually what happens when I try to merge the dataframes. Thanks for the tips! 回答1: Use .merge by

问题 I'm trying to import data to pandas DataFrame, but getting the following error while trying to convert the date_time column to datetime object: pandas.tslib.OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 1-01-19 00:00:00 The format of the column looks like: Jan 19,17 05:04:50 PM My code is: data['Date_Time'] = to_datetime(data['Date_Time']).dt.strftime('%b %d, %y %H:%M:%S ') What is the problem? 回答1: I think you need to_datetime with parameter format: data = pd.DataFrame({'Date_Time

问题 I have the following data frame: >>> import pandas as pd >>> import numpy as np >>> df_test = pd.DataFrame({'id': [100, 101, 102, 103, 104], 'drive': ['4WD', None, '4WD', None, '2WD']}) >>> print(df_test) id drive 0 100 4WD 1 101 None 2 102 4WD 3 103 None 4 104 2WD And I would like to make a new column is_4x4 , that would be equal to 0, when drive is None , or drive is 2WD . In other cases, I would like the column to be equal to 1. I am using the following code, but the result is not as I

问题 I have the following data frame: >>> import pandas as pd >>> import numpy as np >>> df_test = pd.DataFrame({'id': [100, 101, 102, 103, 104], 'drive': ['4WD', None, '4WD', None, '2WD']}) >>> print(df_test) id drive 0 100 4WD 1 101 None 2 102 4WD 3 103 None 4 104 2WD And I would like to make a new column is_4x4 , that would be equal to 0, when drive is None , or drive is 2WD . In other cases, I would like the column to be equal to 1. I am using the following code, but the result is not as I

问题 I am looking to implement a rolling window on a list, but instead of a fixed length of window, I would like to provide a rolling window list: Something like this: l1 = [5, 3, 8, 2, 10, 12, 13, 15, 22, 28] l2 = [1, 2, 2, 2, 3, 4, 2, 3, 5, 3] get_custom_roling( l1, l2, np.average) and the result would be: [5, 4, 5.5, 5, 6.67, ....] 6.67 is calculated as average of 3 elements 10, 2, 8. I implemented a slow solution, and every idea is welcome to make it quicker :): import numpy as np def get_the

问题 I am looking to implement a rolling window on a list, but instead of a fixed length of window, I would like to provide a rolling window list: Something like this: l1 = [5, 3, 8, 2, 10, 12, 13, 15, 22, 28] l2 = [1, 2, 2, 2, 3, 4, 2, 3, 5, 3] get_custom_roling( l1, l2, np.average) and the result would be: [5, 4, 5.5, 5, 6.67, ....] 6.67 is calculated as average of 3 elements 10, 2, 8. I implemented a slow solution, and every idea is welcome to make it quicker :): import numpy as np def get_the

问题 I am looking to implement a rolling window on a list, but instead of a fixed length of window, I would like to provide a rolling window list: Something like this: l1 = [5, 3, 8, 2, 10, 12, 13, 15, 22, 28] l2 = [1, 2, 2, 2, 3, 4, 2, 3, 5, 3] get_custom_roling( l1, l2, np.average) and the result would be: [5, 4, 5.5, 5, 6.67, ....] 6.67 is calculated as average of 3 elements 10, 2, 8. I implemented a slow solution, and every idea is welcome to make it quicker :): import numpy as np def get_the

问题 This question already has an answer here : Matplotlib: change the colors of the result of group by (1 answer) Closed 7 months ago . Pandas dataframe groupby plot I have a similar dataframe to the one in the above question, but it has around 8 ticker symbols. I've defined a list of colours called 'colors' that correspond with the tickers, but when I do: df.groupby('ticker')['adj_close'].plot(color=colors) all the lines on the plot for each of the tickers are the same colour (i.e. the first