dataframe

问题 I'm trying to create a new column in a dataframe that labels animals that are domesticated with a 1. I'm using a for loop, but for some reason, the loop only picks up the last item in the pets list. dog , cat , and gerbil should all be assigned a 1 under the domesticated column. Anyone have a fix for this or a better approach? df = pd.DataFrame( {'creature': ['dog', 'cat', 'gerbil', 'mouse', 'donkey'] }) pets = ['dog', 'cat', 'gerbil'] for pet in pets: df['domesticated'] = np.where(df[

问题 I have a dataframe with array of struct and inside that another array of struct. Any easy way to select few of the structs in the main array and also few in the nested array without disturbing the structure of the entire dataframe? SIMPLE INPUT: -MainArray ---StructCol1 ---StructCol2 ---StructCol3 ---SubArray ------SubArrayStruct4 ------SubArrayStruct5 ------SubArrayStruct6 SIMPLE OUTPUT: -MainArray ---StructCol1 ---StructCol2 ---SubArray ------SubArrayStruct4 ------SubArrayStruct5 The source

问题 Made an api get request for the historical close prices of a stock for a specified company from the financialmodelingprep api. It returns every recorded date for the stock. The problem is that i need only the last date of the last 5 years, in order to compare it to the financial statements. Does anyone know how to filter the dataset to get the last date of the year, without specifying the exact date? The goal is to export the table to csv format and further combine it with other companies. Is

问题 I have a data frame that looks like the following: ID Loc 1 N 2 A 3 N 4 H 5 H I would like to swap A and H in the column Loc while not touching rows that have values of N, such that I get: ID Loc 1 N 2 H 3 N 4 A 5 A This dataframe is the result of a pipe so I'm looking to see if it's possible to append this operation to the pipe. 回答1: We can try chaining together two calls to ifelse , for a base R option: df <- data.frame(ID=c(1:5), Loc=c("N", "A", "N", "H", "H"), stringsAsFactors=FALSE) df

问题 For df : id Date ITEM_ID TYPE GROUP 0 13710750 2019-07-01 SLM607 O X 1 13710760 2019-07-01 SLM607 O M 2 13710770 2019-07-03 SLM607 O I 3 13710780 2019-09-03 SLM607 O N 4 13667449 2019-08-02 887643 O I 5 13667450 2019-08-02 792184 O I 6 13728171 2019-09-17 SLM607 I I 7 13667452 2019-08-02 794580 O I ... ... ... ... ... ... ... ... ... ... with reproducible example: data = {'id': [13710750, 13710760, 13710770, 13710780, 13667449, 13667450, 13728171, 13667452], 'Date': ['2019-07-01', '2019-07-01

问题 Given an Octave dataframe object created as c = cell(m,n); %populate c... pkg load dataframe df = dataframe(c); (see https://octave.sourceforge.io/dataframe/overview.html), Is it possible to access the underlying cell array? Is it there a conversion mechanism back to cell array? Is it possible to save df to CSV? 回答1: Yes. A dataframe object, like any object, can be converted back into a struct . Once you have the resulting struct, look for the fields x_name to get the column names, and x_data

问题 Following up this question and dataframes, I am trying to convert this Into this (I know it looks the same, but refer to the next code line to see the difference): In pandas, I used the line code teste_2 = (value/value.groupby(level=0).sum()) and in pyspark I tried several solutions; the first one was: df_2 = (df/df.groupby(["age"]).sum()) However, I am getting the following error: TypeError: unsupported operand type(s) for /: 'DataFrame' and 'DataFrame' The second one was: df_2 = (df.filter

问题 From the list below I'm able to remove the non-alphabet characters but fall short all the same. I want the Draw eliminated without affecting the desired outcome. df=pd.DataFrame({'Teams': ['Lakefield United', '101002 Castle FC pk, +½ 1.81 o 3.05 o Un 2 1.92 o', '101003 Draw 3.00 o', 'Boms', '101005 Riverside FC pk 2.11 o 2.86 o Un 2, 2½ 1.78 o', '101006 Draw 3.10 o', 'Barmley', '101011 Colsely Lakers -1, -1½ 2.04 o 1.46 o Un 2½, 3 1.83 o', '101012 Draw 4.40 o',]}) Desired Elements :

问题 When using the default example for displaying a report: df = pd.DataFrame( np.random.rand(100, 5), columns=['a', 'b', 'c', 'd', 'e'] ) profile = ProfileReport(df, title='Pandas Profiling Report', html={ 'style': {'full_width': True}}) the correlations heatmaps are not shown. How can I investigate the warnings from the progress bar? 回答1: The progress bar keeps you informed on the calculations that pandas-profiling does. To view the output, you have several option. The easiest way to view them

问题 When using the default example for displaying a report: df = pd.DataFrame( np.random.rand(100, 5), columns=['a', 'b', 'c', 'd', 'e'] ) profile = ProfileReport(df, title='Pandas Profiling Report', html={ 'style': {'full_width': True}}) the correlations heatmaps are not shown. How can I investigate the warnings from the progress bar? 回答1: The progress bar keeps you informed on the calculations that pandas-profiling does. To view the output, you have several option. The easiest way to view them