series

问题 I have a Pandas DataFrame with a named index. I want to pass it off to a piece off code that takes a DataFrame, a column name, and some other stuff, and does a bunch of work involving that column. Only in this case the column I want to highlight is the index, but giving the index's label to this piece of code doesn't work because you can't extract an index like you can a regular column. For example, I can construct a DataFrame like this: import pandas as pd, numpy as np df=pd.DataFrame({'name

问题 I have a Pandas DataFrame with a named index. I want to pass it off to a piece off code that takes a DataFrame, a column name, and some other stuff, and does a bunch of work involving that column. Only in this case the column I want to highlight is the index, but giving the index's label to this piece of code doesn't work because you can't extract an index like you can a regular column. For example, I can construct a DataFrame like this: import pandas as pd, numpy as np df=pd.DataFrame({'name

问题 I am converting some code from Pandas to pyspark. In pandas, lets imagine I have the following mock dataframe, df: And in pandas, I define a certain variable the following way: value = df.groupby(["Age", "Siblings"]).size() And the output is a series as follows: However, when trying to covert this to pyspark, an error comes up: AttributeError: 'GroupedData' object has no attribute 'size' . Can anyone help me solve this? 回答1: The equivalent of size in pyspark is count: df.groupby(["Age",

问题 I've got a Series of numpy arrays: import pandas as pd import numpy as np pd.Series({10: np.array([[0.72260683, 0.27739317, 0. ], [0.7187053 , 0.2812947 , 0. ], [0.71435467, 0.28564533, 1. ], [0.3268072 , 0.6731928 , 0. ], [0.31941951, 0.68058049, 1. ], [0.31260015, 0.68739985, 0. ]]), 20: np.array([[0.7022099 , 0.2977901 , 0. ], [0.6983866 , 0.3016134 , 0. ], [0.69411673, 0.30588327, 1. ], [0.33857735, 0.66142265, 0. ], [0.33244109, 0.66755891, 1. ], [0.32675582, 0.67324418, 0. ]]), 38: np

问题 I am trying to cluster meteorological stations using R. Stations provide such data as temperature, wind speed, humidity and some more on hourly intervals. I can easily cluster univariate time series using tsclust library, but when I cluster multivariate series I get errors. I have data as a list so each list element is a matrix with time series data of one station (variables are columns and rows are different timestamp). If I run: tsclust(data, k = 2, distance = 'Euclidean', seed = 3247,

问题 I am using pinescript, and I have been trying to figure out why the following code does not work. The console keeps showing that series[integer] cannot output integer. I understand that series is not compatible with non-series values. If this is the case, is there a way to change series[integer] to integer? The following code does not work: x = barssince(crossover(cci,100)) y = barssince(crossover(100,cci)) xy = x-y //in this case the xy value is 9 z = highest(cci, abs(xy)) plot(z) The

问题 I need some help with a data types issue. I'm trying to convert a pandas dataframe, which looks like the following: timestamp number 2018-01-01 1 2018-02-01 0 2018-03-01 5 2018-04-01 0 2018-05-01 6 into a pandas series, which looks exactly like the dataframe, without the column names timestamp and number: 2018-01-01 1 2018-02-01 0 2018-03-01 5 2018-04-01 0 2018-05-01 6 It shouldn't be difficult, but I'm having a little trouble figuring out the way to do it, as I'm a beginner in pandas. It

问题 I need some help with a data types issue. I'm trying to convert a pandas dataframe, which looks like the following: timestamp number 2018-01-01 1 2018-02-01 0 2018-03-01 5 2018-04-01 0 2018-05-01 6 into a pandas series, which looks exactly like the dataframe, without the column names timestamp and number: 2018-01-01 1 2018-02-01 0 2018-03-01 5 2018-04-01 0 2018-05-01 6 It shouldn't be difficult, but I'm having a little trouble figuring out the way to do it, as I'm a beginner in pandas. It

问题 I calculated the first 20 elements of the series - in 2 ways , 1st - forward , 2nd - backward. For this I did - #include <iostream> #include <math.h> using namespace std; float sumSeriesForward(int elementCount) { float sum = 0; for (int i = 0; i < elementCount; ++i) { sum += (float) 1 / (pow(3, i)); } return sum; } float sumSeriesBack(int elementCount) { float sum = 0; for (int i = (elementCount - 1); i >= 0; --i) { sum += (float) 1 / (pow(3, i)); } return sum; } int main() { cout.precision

问题 Some times, i want use read_clipboard to read Series es, and i would have to do: pd.Series(pd.read_clipboard(header=None).values[:,0]) So would it be nice if there was an easier way? I can do it very easily for data-frames, like: pd.read_clipboard() And that's it. But for Series , it's much longer-one-liner. So is there an easier way? That i don't know? Any secretive code? 回答1: Copy this to clipboard: 1 2 3 Better would be to use squeeze=True as an argument. pd.read_clipboard(header=None,