multiple-columns

问题 I have dynamic content flowing into a two-column grid. Flex box is a nice solution, but it forces the rows to be the same height, creating some awkward white space when one has more content. Does anyone know of a lightweight solution that can achieve what is hopefully clearly communicated in the below image? Code for my current solution: .grid-container { display:flex; flex-wrap: wrap; justify-content:space-between; height:auto; height:700px; overflow-y:scroll; } .contained-cell{ width:48.5%;

问题 I am trying to calculate multiple colums from multiple columns in a pandas dataframe using a function. The function takes three arguments -a-, -b-, and -c- and and returns three calculated values -sum-, -prod- and -quot-. In my pandas data frame I have three coumns -a-, -b- and and -c- from which I want to calculate the columns -sum-, -prod- and -quot-. The mapping that I do works only when I have exactly three rows. I do not know what is going wrong, although I expect that it has to do

问题 This question already has answers here : CSS column-count not respected (4 answers) Closed 2 years ago . I am trying to use column-count css property to render div s in 4 columns. But when there are 3n items (3, 6, 9...) in columns wrapper, only three columns get filled, and fourth column is empty! CSS: .columns { -webkit-column-count: 4; /* Chrome, Safari, Opera */ -moz-column-count: 4; /* Firefox */ column-count: 4; } .item { display: inline-block; widtth: 100%; border: 1px solid red; }

问题 This question already has answers here : CSS column-count not respected (4 answers) Closed 2 years ago . I am trying to use column-count css property to render div s in 4 columns. But when there are 3n items (3, 6, 9...) in columns wrapper, only three columns get filled, and fourth column is empty! CSS: .columns { -webkit-column-count: 4; /* Chrome, Safari, Opera */ -moz-column-count: 4; /* Firefox */ column-count: 4; } .item { display: inline-block; widtth: 100%; border: 1px solid red; }

问题 I need help to get the position of the column or another way to read in the column two step left of the column Spannung . Exceldata = pd.read_excel(str(Dateien[0]), header=[2]) print Dateien[0] Spannung = Exceldata.columns[Exceldata.columns.str.contains('Spannung effektiv L1')] print Spannung 回答1: IIUC you can use .get_loc So: pos = Exceldata.columns.get_loc(Spannung[0]) then you can index left: other_col = Exceldata.columns[pos -2] Example: In [169]: df = pd.DataFrame(columns=['hello','world

问题 I need help to get the position of the column or another way to read in the column two step left of the column Spannung . Exceldata = pd.read_excel(str(Dateien[0]), header=[2]) print Dateien[0] Spannung = Exceldata.columns[Exceldata.columns.str.contains('Spannung effektiv L1')] print Spannung 回答1: IIUC you can use .get_loc So: pos = Exceldata.columns.get_loc(Spannung[0]) then you can index left: other_col = Exceldata.columns[pos -2] Example: In [169]: df = pd.DataFrame(columns=['hello','world

问题 i am looking to apply multiply masks on each column of a pandas dataset (respectively to it's properties) in python. In the next step i want to find (a) row(s) in the dataframe that fits all conditions . therefore i have : df Out[27]: DE FL GA IA ID 0 0 1 0 0 0 1 1 0 1 0 1 2 0 0 1 0 0 3 0 1 0 0 0 4 0 0 0 0 0 mask_list = [] for i in range(0,5): if i % 2==0: mask_list.append(df[[i]]>0) else: mask_list.append(df[[i]]<1) concat_frame = pa.DataFrame() for mask in mask_list: concat_frame =pa.concat

问题 i am looking to apply multiply masks on each column of a pandas dataset (respectively to it's properties) in python. In the next step i want to find (a) row(s) in the dataframe that fits all conditions . therefore i have : df Out[27]: DE FL GA IA ID 0 0 1 0 0 0 1 1 0 1 0 1 2 0 0 1 0 0 3 0 1 0 0 0 4 0 0 0 0 0 mask_list = [] for i in range(0,5): if i % 2==0: mask_list.append(df[[i]]>0) else: mask_list.append(df[[i]]<1) concat_frame = pa.DataFrame() for mask in mask_list: concat_frame =pa.concat

问题 I am reading a dataset as below. f = sc.textFile("s3://test/abc.csv") My file contains 50+ fields and I want assign column headers for each of fields to reference later in my script. How do I do that in PySpark ? Is DataFrame way to go here ? PS - Newbie to Spark. 回答1: Here is how to add column names using DataFrame: Assume your csv has the delimiter ','. Prepare the data as follows before transferring it to DataFrame: f = sc.textFile("s3://test/abc.csv") data_rdd = f.map(lambda line: [x for

问题 I am able to add a new column in Panda by defining user function and then using apply. However, I want to do this using lambda ; is there a way around? For Example, df has two columns a and b . I want to create a new column c which is equal to the longest length between a and b . Some thing like: df['c'] = df.apply(lambda x, len(df['a']) if len(df['a']) > len(df['b']) or len(df['b']) ) One approach: df = pd.DataFrame({'a':['dfg','f','fff','fgrf','fghj'], 'b' : ['sd','dfg','edr','df','fghjky']