Here’s a small example to demonstrate this (which only applied to DataFrames, not Series, until Pandas 0.19 where it applies to both): One solution is to sort the index first (Note: some functions require sorted indexes): Note: == is also sensitive to the order of columns, so you may have to use sort_index(axis=1): Note: This can still raise (if the index/columns aren’t … Read more
Since yesterday I’ve had this error when I try to import packages on anaconda : ImportError: Missing required dependencies [‘numpy’] I have tried un-installing Anaconda and Python, switching to Python 2.7 but nothing works it’s still the same error, here is the code I get : Any help is really appreciated thanks !
I have a machine learning classification problem with 80% categorical variables. Must I use one hot encoding if I want to use some classifier for the classification? Can i pass the data to a classifier without the encoding? I am trying to do the following for feature selection: I read the train file:num_rows_to_read = 10000 … Read more
For a dataframe df, one can use any of the following: len(df.index) df.shape[0] df[df.columns[0]].count() (== number of non-NaN values in first column) Code to reproduce the plot:
Use GroupBy.sum:
DataFrame.iterrows is a generator which yields both the index and row (as a Series):
I think you need reset_index for same index values and then comapare – for create new column is better use mask or numpy.where: Also instead + use | because working with booleans. Samples:
If I understand right, you want something like this: (Here I convert the values to numbers instead of strings containing numbers. You can convert them to “1” and “0”, if you really want, but I’m not sure why you’d want that.) The reason your code doesn’t work is because using [‘female’] on a column (the second ‘female’ in your w[‘female’][‘female’]) doesn’t mean “select … Read more
I got this issue on Rasberry PI and found that the root cause was missing library: Then it takes few minute to google the required library source: I have not checked the solution on any other Linux-es but likely the same method should be applicable. So try to import the faulty library first and see … Read more
Pandas doesn’t have the ability to store NaN values for integers. Strictly speaking, you could have a column with mixed data types, but this can be computationally inefficient. So if you insist, you can do