series

I have a DataFrame named df as Order Number Status 1 1668 Undelivered 2 19771 Undelivered 3 100032108 Undelivered 4 2229 Delivered 5 00056 Undelivered I would like to convert the Status column to boolean ( True when Status is Delivered and False when Status is Undelivered) but if Status is neither 'Undelivered' neither 'Delivered' it should be considered as NotANumber or something like that. I would like to use a dict d = { 'Delivered': True, 'Undelivered': False } so I could easily add other string which could be either considered as True or False . You can just use map : In [7]: df = pd

I'm somewhat new to pandas. I have a pandas data frame that is 1 row by 23 columns. I want to convert this into a series? I'm wondering what the most pythonic way to do this is? I've tried pd.Series(myResults) but it complains ValueError: cannot copy sequence with size 23 to array axis with dimension 1 . It's not smart enough to realize it's still a "vector" in math terms. Thanks! It's not smart enough to realize it's still a "vector" in math terms. Say rather that it's smart enough to recognize a difference in dimensionality. :-) I think the simplest thing you can do is select that row

In the artificial data I have created for the MWE below I have tried to demonstrate the essence of a script I have created in R. As can be seen by the graph that gets produced from this code, on one of my conditions I don't have a "No" value to complete the series. I have been told that unless I can make this last column that sadly doesn't have the extra series as thin as the columns else where in the graph I won't be permitted to use these graphs. This is sadly a problem because the script I have written produces hundreds of graphs simultaneously, complete with stats, significance indicators,

Is there a way to remove a NaN values from a panda series? I have a series that may or may not have some NaN values in it, and I'd like to return a copy of the series with all the NaNs removed. Roman Pekar >>> s = pd.Series([1,2,3,4,np.NaN,5,np.NaN]) >>> s[~s.isnull()] 0 1 1 2 2 3 3 4 5 5 update or even better approach as @DSM suggested in comments, using pandas.Series.dropna() : >>> s.dropna() 0 1 1 2 2 3 3 4 5 5 A small usage of np.nan ! = np.nan s[s==s] Out[953]: 0 1.0 1 2.0 2 3.0 3 4.0 5 5.0 dtype: float64 More Info np.nan == np.nan Out[954]: False 来源： https://stackoverflow.com/questions

This question already has an answer here: How to remove items from a list while iterating? 26 answers When I am writing code in Python, I often need to remove items from a list or other sequence type based on some criteria. I haven't found a solution that is elegant and efficient, as removing items from a list you are currently iterating through is bad. For example, you can't do this: for name in names: if name[-5:] == 'Smith': names.remove(name) I usually end up doing something like this: toremove = [] for name in names: if name[-5:] == 'Smith': toremove.append(name) for name in toremove: