pandas

问题 I'm trying to figure out if there is a good way to manage units in my pandas data. For example, I have a DataFrame that looks like this: length (m) width (m) thickness (cm) 0 1.2 3.4 5.6 1 7.8 9.0 1.2 2 3.4 5.6 7.8 Currently, the measurement units are encoded in column names. Downsides include: column selection is awkward -- df['width (m)'] vs. df['width'] things will likely break if the units of my source data change If I wanted to strip the units out of the column names, is there somewhere

问题 I'm trying to figure out if there is a good way to manage units in my pandas data. For example, I have a DataFrame that looks like this: length (m) width (m) thickness (cm) 0 1.2 3.4 5.6 1 7.8 9.0 1.2 2 3.4 5.6 7.8 Currently, the measurement units are encoded in column names. Downsides include: column selection is awkward -- df['width (m)'] vs. df['width'] things will likely break if the units of my source data change If I wanted to strip the units out of the column names, is there somewhere

问题 I have a DataFrame with the following columns and no duplicates: ['region', 'type', 'name', 'value'] that can be seen as a hierarchy as follows grouped = df.groupby(['region','type', 'name']) I would like to serialize this hierarchy as a JSON object. If anyone is interested, the motivation behind this is to eventually put together a visualization like this one which requires a JSON file. To do so, I need to convert grouped into the following: new_data['children'][i]['name'] = region new_data[

问题 I have a DataFrame with the following columns and no duplicates: ['region', 'type', 'name', 'value'] that can be seen as a hierarchy as follows grouped = df.groupby(['region','type', 'name']) I would like to serialize this hierarchy as a JSON object. If anyone is interested, the motivation behind this is to eventually put together a visualization like this one which requires a JSON file. To do so, I need to convert grouped into the following: new_data['children'][i]['name'] = region new_data[

问题 I'm learning python. As practice I'm building a rss scraper with feedparser putting the output into a pandas dataframe and trying to mine with NLTK...but I'm first getting a list of articles from multiple RSS feeds. I used this post on how to pass multiple feeds and combined it with an answer I got previously to another question on how to get it into a Pandas dataframe. What the problem is, I want to be able to see the data from all the feeds in my dataframe. Currently I'm only able to access

问题 I am using pandas.read_csv to read a whitespace delimited file. The file has a variable number of whitespace characters in front of every line (the numbers are right-aligned). When I read this file, it creates a column of NaN. Why does this happen, and what is the best way to prevent it? Example: Text file: 9.0 3.3 4.0 32.3 44.3 5.1 7.2 1.1 0.9 Command: import pandas as pd pd.read_csv("test.txt",delim_whitespace=True,header=None) Output: 0 1 2 3 0 NaN 9.0 3.3 4.0 1 NaN 32.3 44.3 5.1 2 NaN 7.2

问题 I am using pandas.read_csv to read a whitespace delimited file. The file has a variable number of whitespace characters in front of every line (the numbers are right-aligned). When I read this file, it creates a column of NaN. Why does this happen, and what is the best way to prevent it? Example: Text file: 9.0 3.3 4.0 32.3 44.3 5.1 7.2 1.1 0.9 Command: import pandas as pd pd.read_csv("test.txt",delim_whitespace=True,header=None) Output: 0 1 2 3 0 NaN 9.0 3.3 4.0 1 NaN 32.3 44.3 5.1 2 NaN 7.2

问题 I have a dataframe as follows. I need to do ANOVA on this between three conditions. The dataframe looks like: data0 = pd.DataFrame({'Names': ['CTA15', 'CTA15', 'AC007', 'AC007', 'AC007','AC007'], 'value': [22, 22, 2, 2, 2,5], 'condition':['NON', 'NON', 'YES', 'YES', 'RE','RE']}) I need to do ANOVA test between YES and NON, NON and RE and YES and RE, conditions from conditions for Names. I know I could do it like this, NON=df.query('condition =="NON"and Names=="CTA15"') no=df.value YES=df

问题 Suppose I have a dataframe as follows: In [1]: test_dup_df Out[1]: exe_price exe_vol flag 2008-03-13 14:41:07 84.5 200 yes 2008-03-13 14:41:37 85.0 10000 yes 2008-03-13 14:41:38 84.5 69700 yes 2008-03-13 14:41:39 84.5 1200 yes 2008-03-13 14:42:00 84.5 1000 yes 2008-03-13 14:42:08 84.5 300 yes 2008-03-13 14:42:10 84.5 88100 yes 2008-03-13 14:42:10 84.5 11900 yes 2008-03-13 14:42:15 84.5 5000 yes 2008-03-13 14:42:16 84.5 3200 yes I want to group a duplicate data at time 14:42:10 and apply

问题 I have this data: ID TIME 1 2 1 4 1 2 2 3 I want to group the data by ID and calculate the mean time and the size of each group. ID MEAN_TIME COUNT 1 2.67 3 2 3.00 1 If I run this code, then I get an error "ValueError: cannot insert ID, already exists": result = df.groupby(['ID']).agg({'TIME': 'mean', 'ID': 'count'}).reset_index() 回答1: Use parameter drop=True which not create new column with index but remove it: result = df.groupby(['ID']).agg({'TIME': 'mean', 'ID': 'count'}).reset_index(drop