series

I'm using a line chart with JavaFX: LineChart<Number, Number> lineChart = new LineChart<>(xAxis, yAxis); XYChart.Series series = new XYChart.Series(); lineChart.getData().add(series); I want to add data to the series successively, thereby the order in which the values arrive can not be predicted. For example, the index can be between 0 and the current size of the series. series.getData().add(new XYChart.Data(index, value)); Consider the following scenario: //initializing... series.getData().add(new XYChart.Data(1, 400)); series.getData().add(new XYChart.Data(3, 500)); series.getData().add(new

ykp.data Out[182]: state action reward 0 [41] 5 59 1 [5] 52 48 2 [46] 35 59 3 [42] 16 12 4 [43] 37 48 5 [36] 5 59 6 [49] 52 48 7 [39] 11 23 I would like to find the row that matches [42] in the state entry so I ran ykp.data.query('state == [42]') but I get Empty DataFrame Columns: [state, action, reward] Index: [] when I should be seeing [42], 16, 12 . Can someone please tell me how I can workaround this? I need my state-values to be stored as arrays. Best to avoid pd.Series.apply here. Instead, you can use itertools.chain to construct a regular NumPy array. Then compare the array to an

I am dynamically creating a couple of gvisScatterChart s. I want to define the colors of each line, which I can do using series and the color attribute. There is nothing like an order or a fix number by which I can predefine the colors. So I want to create an array of attributes parallel to my colors and just place it at series=myColors . The problem is that gVis expects a string like: series="[{color: 'black', visibleInLegend: false}]", As soon as I create a string using the paste function gVis doesn't accept them any more and just shows a blank page as chart. (Even when marking the " using \

I have a pandas Series with an integer index which I've sorted (by value), how I access values by position in this Series. For example: s_original = pd.Series({0: -0.000213, 1: 0.00031399999999999999, 2: -0.00024899999999999998, 3: -2.6999999999999999e-05, 4: 0.000122}) s_sorted = np.sort(s_original) In [3]: s_original Out[3]: 0 -0.000213 1 0.000314 2 -0.000249 3 -0.000027 4 0.000122 In [4]: s_sorted Out[4]: 2 -0.000249 0 -0.000213 3 -0.000027 4 0.000122 1 0.000314 In [5]: s_sorted[3] Out[5]: -2.6999999999999999e-05 But I would like to get the value 0.000122 i.e. the item in position 3. How

I want to travel round the series index In [44]: type(ed1) Out[44]: pandas.core.series.Series In [43]: for _, row in ed1.iterrows(): ...: print(row.name) and I get thie error: AtributeError: 'Series' ojbect has no attribute 'iterrows' Is series has any methods like iterrows? thank a lot Series objects define an iteritems method (the data is returned as a iterator of index-value pairs. for _, val in ed1.iteritems(): ... Alternatively, you can iterate over a list by calling tolist , for val in ed1.tolist(): ... Word of advice, iterating over pandas objects is generally discouraged. Wherever

I created a multiIndex DataFrame by: df.set_index(['Field1', 'Field2'], inplace=True) If this is not a multiIndex DataFrame please tell me how to make one. I want to: Group by the same columns that are in the index Aggregate a count of each group Then return the whole thing as a Series with Field1 and Field2 as the index How do I go about doing this? ADDITIONAL INFO I have a multiIndex dataFrame that looks like this: Continent Sector Count Asia 1 4 2 1 Australia 1 1 Europe 1 1 2 3 3 2 North America 1 1 5 1 South America 5 1 How can I return this as a Series with the index of [Continent, Sector

I have dataframe with following columns: Name, Surname, dateOfBirth, city, country I am interested to find what is most common combination of name and surname and how much it occurs as well. Would be nice also to see list of top 10 combinations. My idea for top one was: mostFreqComb= df.groupby(['Name','Surname'])['Name'].count().argmax() But I think it is not giving me correct answer. Help would be much appreciated ! Thanks, Neb For performance implications of the below solutions, see Pandas groupby.size vs series.value_counts vs collections.Counter with multiple series . They are presented

This question already has an answer here: Combining two Series into a DataFrame in pandas 7 answers I have two series in pandas. series 1: id count_1 1 3 3 19 4 15 5 5 6 2 and series 2: id count_2 1 3 3 1 4 1 5 2 6 1 How do I combine the tables along the ids to form the below? id count_1 count_2 1 3 3 3 19 1 4 15 1 5 5 2 6 2 1 You can use concat : In [11]: s1 Out[11]: id 1 3 3 19 4 15 5 5 6 2 Name: count_1, dtype: int64 In [12]: s2 Out[12]: id 1 3 3 1 4 1 5 2 6 1 Name: count_2, dtype: int64 In [13]: pd.concat([s1, s2], axis=1) Out[13]: count_1 count_2 id 1 3 3 3 19 1 4 15 1 5 5 2 6 2 1 Note:

Lets say I have r = pd.DataFrame({'A':1 , 'B':pd.Series(1,index=list(range(4)),dtype='float32')}) And r['B'].describe()[['mean','std','min','max']] gives an output : mean 1.0 std 0.0 min 1.0 max 1.0 Name: B, dtype: float64 But from the above output , how should I get rid or suppress the last line " Name:B, dtype: float64 " I figured out one way to achieve this x=r['B'].describe()[['mean','std','min','max']] print "mean ",x['mean'],"\nstd ",x['std'],"\nmin ",x['min'],"\nmax ",x['max'] which gives the desired output : mean 1.0 std 0.0 min 1.0 max 1.0 Is there any cleaner to achieve this output