You can use 2 times cumsum(): # reset val desired_col #0 0 1 1 #1 0 5 6 #2 0 4 10 #3 1 2 2 #4 1 -1 -1 #5 0 6 5 #6 0 4 9 #7 1 2 2 df[‘cumsum’] = df[‘reset’].cumsum() #cumulative sums of groups to column des df[‘des’]= df.groupby([‘cumsum’])[‘val’].cumsum() print … Read more
Another option is to set your child div to display: inline-block; .content { display: inline-block; height: 100%; width: 100%; background-color: blue; } .container { display: table; } .child { width: 30px; background-color: red; display: table-cell; vertical-align: top; } .content { display: inline-block; height: 100%; width: 100%; background-color: blue; } <div class=”container”> <div class=”child”> a <br … Read more
There are multiple ways to do this. You can use a sub query: SET @some_var = (SELECT COUNT(*) FROM mytable); (like your original, just add parenthesis around the query) or use the SELECT INTO syntax to assign multiple values: SELECT COUNT(*), MAX(col) INTO @some_var, @some_other_var FROM tab; The sub query syntax is slightly faster (I … Read more
Ok, I THINK I understand what you want now, and let me clarify to confirm before the query. You want 1 record for each user. For each user, you want their BEST POINTS score record. Of the best points per user, you want the one with the best average time. Once you have all users … Read more
You’re binding the ItemsSource to a property in the DataContext called Items, so to update the collection, you need to go to the Items property in the DataContext and clear it. In addition, the Items property needs to be of type ObservableCollection, not List if you want it to update the UI whenever the underlying … Read more
You can use function map and select by function np.where more info print df # a b #0 aaa rrrr #1 bb k #2 ccc e #condition if condition is True then len column a else column b df[‘c’] = np.where(df[‘a’].map(len) > df[‘b’].map(len), df[‘a’].map(len), df[‘b’].map(len)) print df # a b c #0 aaa rrrr 4 … Read more
Say your current order of column is [b,c,d,a] and you want to order it into [a,b,c,d], you could do it this way: new_df = old_df[[‘a’, ‘b’, ‘c’, ‘d’]]
What we can do is use nunique to calculate the number of unique values in each column of the dataframe, and drop the columns which only have a single unique value: In [285]: nunique = df.nunique() cols_to_drop = nunique[nunique == 1].index df.drop(cols_to_drop, axis=1) Out[285]: index id name data1 0 0 345 name1 3 1 1 … Read more
With a multi-index we have to specify the column using a tuple in order to drop a specific column, or specify the level to drop all columns with that key on that index level. Instead of saying drop column ‘c’ say drop (‘a’,’c’) as shown below: df.drop((‘a’, ‘c’), axis = 1, inplace = True) Or … Read more