dataframe

问题 I have a dataframe with text scraped online in each row which contains sports selection information (all in the same column). I am trying to transpose the data so that: print(df): Col A Random text sentence Random text sentence Random text sentence Race 1 - Handicap 14 - NAME 3 - NAME 5 - NAME 6 - NAME Race Overview: lorem ipsum etc etc Race 2 - Sprint 12 - NAME 10 - NAME 8 - NAME 11 - NAME Race Overview: Second lorem ipsum etc etc Becomes: Race Name | Selection No | Selection | Race Overview

问题 Hi everyone I need a little bit of help with a problem I'm facing, which I'm sure is quite simple but I can't seem to be able to solve it by myself. Basically this is my dataset: Age Gender Group V1 V2 V3 V4 V5 20 1 1 2 1 4 21 2 1 2 2 1 35 2 2 2 1 22 2 1 2 I see that many suggest subset/select function to perform analysis with specific variables, but what I need is to work from v1 to v5 to understand how many row to delete cause of the missing data but without losing the age, gender and group

问题 I need to research something about what are the most cost efficient movie genres. My problem is that the genres are provided all within one string: This gives me about 300 different unique categories. How can I split these into about 12 original dummy genre columns so I can analyse each main genre? 回答1: Thanks to Yong Wang who suggested the get_dummies function within pandas. We can shorten the code significantly: df = pd.DataFrame({ 'movie_id': range(5), 'gernes': [ 'Action|Adventure|Fantasy

问题 I need to research something about what are the most cost efficient movie genres. My problem is that the genres are provided all within one string: This gives me about 300 different unique categories. How can I split these into about 12 original dummy genre columns so I can analyse each main genre? 回答1: Thanks to Yong Wang who suggested the get_dummies function within pandas. We can shorten the code significantly: df = pd.DataFrame({ 'movie_id': range(5), 'gernes': [ 'Action|Adventure|Fantasy

问题 i´m currently working with a large dataframe of 75 columns and round about 9500 rows. This dataframe contains observations for every day from 1995-2019 for several observation points. Edit: The print from dput(head(df)) > dput(head(df)) structure(list(date = structure(c(9131, 9132, 9133, 9134, 9135, 9136), class = "Date"), x1 = c(50.75, 62.625, 57.25, 56.571, 36.75, 39.125), x2 = c(62.25, 58.714, 49.875, 56.375, 43.25, 41.625), x3 = c(90.25, NA, 70.125, 75.75, 83.286, 98.5), x4 = c(60, 72, 68

问题 i´m currently working with a large dataframe of 75 columns and round about 9500 rows. This dataframe contains observations for every day from 1995-2019 for several observation points. Edit: The print from dput(head(df)) > dput(head(df)) structure(list(date = structure(c(9131, 9132, 9133, 9134, 9135, 9136), class = "Date"), x1 = c(50.75, 62.625, 57.25, 56.571, 36.75, 39.125), x2 = c(62.25, 58.714, 49.875, 56.375, 43.25, 41.625), x3 = c(90.25, NA, 70.125, 75.75, 83.286, 98.5), x4 = c(60, 72, 68

问题 I need to compare two columns together: "EMAIL" and "LOCATION". I'm using Email because it's more accurate than name for this issue. My objective is to find total number of locations each person worked at, sum up the total of locations to select which sheet the data will been written to and copy the original data over to the new sheet(tab). I need the original data copied over with all the duplicate locations, which is where this problem stumps me. Full Excel Sheet Had to use images because

问题 I need to compare two columns together: "EMAIL" and "LOCATION". I'm using Email because it's more accurate than name for this issue. My objective is to find total number of locations each person worked at, sum up the total of locations to select which sheet the data will been written to and copy the original data over to the new sheet(tab). I need the original data copied over with all the duplicate locations, which is where this problem stumps me. Full Excel Sheet Had to use images because

问题 I have (in my opinion) a strange problem with python pandas. If I do: cc1 = cc.copy(deep=True) for the dataframe cc and than ask a certain row and column: print(cc1.loc['myindex']['data'] is cc.loc['myindex']['data']) I get True What's wrong here? 回答1: Deep copying doesn't work in pandas and the devs consider putting mutable objects inside a DataFrame as an antipattern There is nothing wrong in your code, just in case if you want to know the difference with some example of deep and shallow

问题 I have (in my opinion) a strange problem with python pandas. If I do: cc1 = cc.copy(deep=True) for the dataframe cc and than ask a certain row and column: print(cc1.loc['myindex']['data'] is cc.loc['myindex']['data']) I get True What's wrong here? 回答1: Deep copying doesn't work in pandas and the devs consider putting mutable objects inside a DataFrame as an antipattern There is nothing wrong in your code, just in case if you want to know the difference with some example of deep and shallow