pandas

问题 An extension to : Removing list of words from a string I have following dataframe and I want to delete frequently occuring words from df.name column: df : name Bill Hayden Rock Clinton Bill Gates Vishal James James Cameroon Micky James Michael Clark Tony Waugh Tom Clark Tom Bill Avinash Clinton Shreyas Clinton Ramesh Clinton Adam Clark I'm creating a new dataframe with words and their frequency with following code : df = pd.DataFrame(data.name.str.split(expand=True).stack().value_counts()) df

问题 I want to count number of code by month. This is my example dataframe. id month code 0 sally 0 s_A 1 sally 0 s_B 2 sally 0 s_C 3 sally 0 s_D 4 sally 0 s_E 5 sally 0 s_A 6 sally 0 s_A 7 sally 0 s_B 8 sally 0 s_C 9 sally 0 s_A I transformed to this Series using count(). df.groupby(['id', 'code', 'month']).month.count() id code month count sally s_A 0 12 1 10 2 3 7 15 But, I want to include zero occurrence, like this. id code month count sally s_A 0 12 1 10 2 3 3 0 4 0 5 0 6 0 7 15 8 0 9 0 10 0

问题 I have following dataframe import pandas as pd data=['5Star','FiveStar','five star','fiv estar'] data = pd.DataFrame(data,columns=["columnName"]) When I try to filter with one condition it works fine. data[data['columnName'].str.contains("5")] Output: columnName 0 5Star But It gives an error when doing with multiple conditions. How to filter it for conditions five and 5 ? Expected Output: columnName 0 5Star 2 five star 回答1: Use str.contains with a string with values separated by '|' : print

问题 I have a DataFrame ( df ) that looks like the following: +----------+----+ | dd_mm_yy | id | +----------+----+ | 01-03-17 | A | | 01-03-17 | B | | 01-03-17 | C | | 01-05-17 | B | | 01-05-17 | D | | 01-07-17 | A | | 01-07-17 | D | | 01-08-17 | C | | 01-09-17 | B | | 01-09-17 | B | +----------+----+ This the end result i would like to compute: +----------+----+-----------+ | dd_mm_yy | id | cum_count | +----------+----+-----------+ | 01-03-17 | A | 1 | | 01-03-17 | B | 1 | | 01-03-17 | C | 1 |

问题 I have following dataframe import pandas as pd data=['5Star','FiveStar','five star','fiv estar'] data = pd.DataFrame(data,columns=["columnName"]) When I try to filter with one condition it works fine. data[data['columnName'].str.contains("5")] Output: columnName 0 5Star But It gives an error when doing with multiple conditions. How to filter it for conditions five and 5 ? Expected Output: columnName 0 5Star 2 five star 回答1: Use str.contains with a string with values separated by '|' : print

问题 My dataframe looks like this: datetime1 datetime2 datetime3 datetime4 id 1 5 6 5 5 2 7 2 3 5 3 4 2 3 2 4 6 4 4 7 5 7 3 8 9 and I have a numpy array like this: index_arr = [3, 2, 0, 1, 2] This numpy array refers to the index in each row, respectively, that I want to replace. The values I want to use in the replacement are in another numpy array: replace_arr = [14, 12, 23, 17, 15] so that the updated dataframe looks like this: datetime1 datetime2 datetime3 datetime4 id 1 5 6 5 14 2 7 2 12 5 3

问题 I have Alcohol_df dataframe in which qualification is a column. I have created a list as follows: Graduate_list=['B.tech','b.tech','b-tech','Btech','BE', 'B.E', 'b.e','BACHELOR','bachelor','BSc', 'Bsc','bsc','BSC','BBM'] I did Alcohol_df['qualification'].isin(Graduate_list) to find which columns contains the elements from the list. I want to do some operation on the dataframe if its value is in the list.I did if ((Alcohol_df['qualification'].isin(Graduate_list)): But getting this error

问题 I have a pandas dataframe that looks as follows: ID round player1 player2 1 1 A B 1 2 A C 1 3 B D 2 1 B C 2 2 C D 2 3 C E 3 1 B C 3 2 C D 3 3 C A The dataframe contains sport match results, where the ID column denotes one tournament, the round column denotes the round for each tournament, and player1 and player2 columns contain the names of players that played against eachother in the respective round . I now want to cumulatively count the tournament participations for, say, player A . In

问题 Here is my code: data=pd.get_dummies(data['movie_id']).groupby(data['user_id']).apply(max) df=pd.DataFrame(data) replace=df.replace(0,np.NaN) t=replace.fillna(-1) sparse=sp.csr_matrix(t.values) My data consist of two columns which are movie_id and user_id. user_id movie_id 5 1000 6 1007 I want to convert the data to a sparse matrix. I first created an interaction matrix where rows indicate user_id and columns indicate movie_id with positive interaction as +1 and negative interaction as -1.

问题 Here is my code: data=pd.get_dummies(data['movie_id']).groupby(data['user_id']).apply(max) df=pd.DataFrame(data) replace=df.replace(0,np.NaN) t=replace.fillna(-1) sparse=sp.csr_matrix(t.values) My data consist of two columns which are movie_id and user_id. user_id movie_id 5 1000 6 1007 I want to convert the data to a sparse matrix. I first created an interaction matrix where rows indicate user_id and columns indicate movie_id with positive interaction as +1 and negative interaction as -1.