One way is to use the transform function to fill the value column after group by: import pandas as pd a[‘value’] = a.groupby(‘company’)[‘value’].transform(lambda v: v.ffill()) a # company value #level_1 #2010-01-01 a 1.0 #2010-01-01 b 12.0 #2011-01-01 a 2.0 #2011-01-01 b 12.0 #2012-01-01 a 2.0 #2012-01-01 b 14.0 To compare, the original data frame looks … Read more
You can follow the recommendation in PEP 3101 and use a subclass Formatter: import string class BlankFormatter(string.Formatter): def __init__(self, default=””): self.default=default def get_value(self, key, args, kwds): if isinstance(key, str): return kwds.get(key, self.default) else: return string.Formatter.get_value(key, args, kwds) kwargs = {“name”: “mark”, “adj”: “mad”} fmt=BlankFormatter() print fmt.format(“My name is {name} and I’m really {adj}.”, **kwargs) # … Read more
The reason is that max works by taking the first value as the “max seen so far”, and then checking each other value to see if it is bigger than the max seen so far. But nan is defined so that comparisons with it always return False — that is, nan > 1 is false … Read more
The problem is that those { and } characters you have there don’t specify a key for formatting. You need to double them up, so change your code to: addr_list_formatted.append(“”” “{0}” {{ “gamedir” “str” “address” “{1}” }} “””.format(addr_list_idx, addr))
You can use addNA(). x <- c(1, 1, 2, 2, 3, NA) addNA(x) # [1] 1 1 2 2 3 <NA> # Levels: 1 2 3 <NA> This is basically a convenience function for factoring with exclude = NULL. From help(factor) – addNA modifies a factor by turning NA into an extra level (so that … Read more
df <- data.frame(A = 1:10, B = 11:20, c = 21:30) head(df) ## A B c ## 1 1 11 21 ## 2 2 12 22 ## 3 3 13 23 ## 4 4 14 24 ## 5 5 15 25 ## 6 6 16 26 as.data.frame(lapply(df, function(cc) cc[ sample(c(TRUE, NA), prob = c(0.85, 0.15), … Read more
You need to read the documentation (emphasis added): Return object with labels on given axis omitted dropna returns a new DataFrame. If you want it to modify the existing DataFrame, all you have to do is read further in the documentation: inplace : boolean, default False If True, do operation inplace and return None. So … Read more