series

Does anyone know how to or found any good examples of explicitly setting the color of the data points series when using the WPFToolkit chart control? I would like to set this as a style in my XAML. You can set the Palette on the Chart. This example is for a ColumnSeries, but you can adapt it for whatever type you are using. <charting:Chart ... Palette="{StaticResource MyPalette}"> The Palette definition looks like this: <datavis:ResourceDictionaryCollection x:Key="MyPalette"> <ResourceDictionary> <Style x:Key="DataPointStyle" BasedOn="{StaticResource ColumnSeries1Style}" TargetType="Control" /

I'm seeking advice for a problem that I thought to be simple, and it might be simple indeed by creating a small script, but I think there should already be a way to do that with git/quilt/stgit. I'm not exactly good at git and this is causing some issues to me. My problem: I've got a git tree (linux kernel) and a number of patches. What happens, such patches were intended for and older version of the kernel, and many of them have already been applied to my tree. The patches start with an header line like From b1af4315d823a2b6659c5b14bc17f7bc61878ef4 (timestamp) and by doing something like git

If I have a pandas dataframe that looks like this: Sequence Rating 0 HYHIVQKF 1 1 YGEIFEKF 2 2 TYGGSWKF 3 3 YLESFYKF 4 4 YYNTAVKL 5 5 WPDVIHSF 6 This is the code that I am using the return the rows that match the following pattern: \b.[YF]\w+[LFI]\b pat = r'\b.[YF]\w+[LFI]\b' new_df.Sequence.str.contains(pat) new_df[new_df.Sequence.str.contains(pat)] The above code is returning the rows that match the pattern, but what can I use to return the unmatched rows? Expected Output: Sequence Rating 1 YGEIFEKF 2 3 YLESFYKF 4 5 WPDVIHSF 6 You can just do a negation of your existing Boolean series: df[

I'm working with pandas and I used the groupby: group = df_crimes_query.groupby(["CrimeDateTime", "WeaponFactor"]).size() group.head(20) CrimeDateTime WeaponFactor 2016-01-01 FIREARM 11 HANDS 26 KNIFE 3 OTHER 11 UNDEFINED 102 2016-01-02 FIREARM 10 HANDS 21 KNIFE 8 OTHER 6 UNDEFINED 68 2016-01-03 FIREARM 12 HANDS 13 KNIFE 6 OTHER 5 UNDEFINED 73 2016-01-04 FIREARM 11 HANDS 10 KNIFE 1 OTHER 3 UNDEFINED 84 dtype: int64 The type of it is a Series: type(group) pandas.core.series.Series I would like a dataframe about like this: CrimeDateTime FIREARM HANDS KNIFE OTHER UNDEFINED 2016-01-01 11 26 3 11

Despite of the numerous stack overflow questions on appending data to a dataframe I could not really find an answer to the following. I am looking for a straight forward solution to append a list as last row of a dataframe. Imagine I have a simple dataframe: indexlist=['one'] columnList=list('ABC') values=np.array([1,2,3]) # take care, the values array is a 3x1 size array. # row has to be 1x3 so we have to reshape it values=values.reshape(1,3) df3=pd.DataFrame(values,index=indexlist,columns=columnList) print(df3) A B C one 1 2 3 After some operations I get the following list: listtwo=[4,5,6] I

I want to plot a pandas series which index is incountinuous DatatimeIndex. My code is as follows: import matplotlib.dates as mdates index = pd.DatetimeIndex(['2000-01-01 00:00:00', '2000-01-01 00:01:00', '2000-01-01 00:02:00', '2000-01-01 00:03:00', '2000-01-01 00:07:00', '2000-01-01 00:08:00'], dtype='datetime64[ns]') df = pd.Series(range(6), index=index) print(df) plt.plot(df.index, df.values) plt.gca().xaxis.set_major_formatter(mdates.DateFormatter("%M")) plt.show() The output is: But the result is not what I really want, because 2000-01-01 00:04:00 is also plotted on the image. The desired

I have a function which takes an array and a value, and returns a value. I would like to apply it to my Series s on a rolling basis, so the array is always the rolling window. Here's a minimal example of what I've tried (unsuccessfully), using np.random.choice in place of my real function. I find lots of examples for finding rolling means and other built-in functions, but can't get it to work for my arbitrary lambda function. s = pd.Series([1,2,3,4,5,6,7,8,9]) rolling_window = s.rolling(3) First attempt: new_values = s.apply(lambda x: np.random.choice(rolling_window, size=1)) ValueError: a [a