Pandas rolling skip nan. ix[2,'A'], take the mean of last 3 valid data 4 3.
Pandas rolling skip nan mean) 0 1. shift(-4). Note that the return type is a multi-indexed series, which is different from previous (deprecated) pd. shift(-5),5) print . Here is my isssue: import pandas as pd import numpy as np >>> df = pd. This argument is only implemented when specifying engine='numba' in the method call. 'numba': Runs the operation through JIT compiled code from numba. But my case is the opposite. plot() pandas. 5 while currently it returns NaN. 0 7. DataFrame({'A': [np. 116775 5 4. 0 10. api. B - the offset is set as the index of the DataFrame) offset X Y Z 0 -0. std(). I landed here in search of a fast (vectorized) way of doing this, but did not find it. nan, 5, np. fillna(pd. – I have never used sampling and there might be better solutions out there which could simply ignore the "group" based on "condition". nan,5,6]) eA = A[A. mean() df. 333333 8 Execute the rolling operation per single column or row ('single') or over the entire object ('table'). df Out[8]: A1 A2 A3 0 4. 6. pandas. Given a large mutli-column Pandas dataframe, I would like to compute the rolling "k-mean" over a window of N elements as quickly as possible. a == 1, 'A', 'B') print(df) Out[60]: a b 0 1 A 1 1 A 2 1 A 3 1 A 4 1 A 5 2 B 6 1 A 7 2 B 8 2 B 9 2 B 10 2 B def get_mode_from_Series(series): return series. 0 5 4. rolling(window=3). import numpy as np import pandas as pd result = data. mean(skipna=True). Viewed 4k times 0 . this is done with rolling() objects. Site Navigation pandas. random. For example, assuming adjust=True, if ignore_na=False, the weighted average of 3, NaN, 5 would be calculated as Pandas rolling gives NaN. How to put NaN in Pandas Dataframe efficiently? 2. 0 29. window. A>df. Pandas rolling mean only for non-NaNs. std ([1,2,2,7,2], ddof=1). I think that pandas rolling mean will cover the case you gave. nanpercentile, which explicitely Computes the qth percentile of the data along the specified axis, while ignoring nan values (quoted from the docs, my emphasis): >>> dfAB A B 0 5. 05, 0. 0 Pandas: Rolling Mean and ignore NaN. Series([1,2,3,4,np. For instance, if the groupby returns [2, NaN, 1], the result should be 1. Similarly I don't get why there are 5 and not 4 items in the rm-5 column that are NaN. 333333 We can center-align the operation like this: You can simply multiply the input array with the weights and sum along the specified axis ignoring NaNs with np. Pandas - handling NaN. Using pandas 0. Data for for every month of January is missing, however (NaN), so I am using. e. This is working for the start but is pulling NANs at the end. rolling(window=12). Stack Overflow. rolling. 0 7508. The easiest fix (without understanding more about how the skewness is to be applied) would be to create a copy of col to work on:. nan, np. Doing so will return a result riddled with more nans. 0 However, after testing with dataframes with both even and odd-numbers of rows I saw that quantile handled these situations properly. ix[2,'A'], take the mean of last 3 valid data 4 3. Back to top Ctrl+K. fruit date amount 0 apple 2017-06-01 NaN 1 apple 2017-06-02 NaN 2 apple 2017-06-03 17. 0 9049. 0 3 1. Returns: pandas. 531939 The first std value (2. Hot Network Questions pandas rolling functions per group. groupby. – There's no such built in argument in the rolling function, but you can compute the usual rolling function and then skip every nth row (where n=2 in your case). date() f = web. Price. Drop NaN but keep None. nan() to find average of each row and column. In case I wanted to create a column with the last non-NaN in the rolling function, how can I adjust the function, since the rolling function also does not support . If an integer, the fixed number of observations used for each window. In [53]: df Out[53]: 1 0 2007-01-01 07:00:00 6. 0 83. I understand that in older Differently from DataFrameGroupBy aggregation functions, where NaNs are skipped by default (skipna=True), this is not the case for Rolling aggregation functions. nan). NaN x2 = pandas. count (numeric_only = False) [source] # Calculate the rolling count of non NaN observations. rolling method as commented by @kekert). An instance of Window is returned if win_type is passed. 0 NaN NaN B NaN 1. 0 2. 0 With the following correlation results: 💡 Problem Formulation: In data analysis, a common task is to perform operations over a sliding window of a data series, such as calculating moving averages or smoothed values. We can verify that by removing the those values and checking the results. The internal mean() function will ignore NaN values. 0. My following attempt returns all NaNs. 0 Pandas - rolling average is giving a NaN column? How to ignore NaN when applying rolling with Pandas. The problem seems to arise only when NaN values are present in the dataframe. Here "k-mean" is defined as the mean of the N-2k elements of N which exclude the k largest and k smallest elements. 1. 0 2 NaN 3 NaN 10 2. Rolling mean, returning nan in dataframe pandas python. Write better code with AI Saturday, July 7, 2018 12:41:43 PM To: pandas-dev/pandas Cc: Tom Augspurger; Comment Subject: Re: [pandas Currently I have the DataFrame seen below and I want to do a rolling average over the last 10 occurrences that have actual values, but to skip the NaNs Example DataFrame The issues is that if I run df['AST_Hit']. mean() df Price New 0 63 NaN Pandas: Rolling Mean and ignore NaN. nan it could work I guess. The only point where we get NaN, is when the only value is NaN. A. 000000 3 5. use_numba You should be providing a value to min_periods. diff (periods = 1, axis = 0) [source] # First discrete difference of element. 2. 0 # B column is valid 3 2. Example Execute the rolling operation per single column or row ('single') or over the entire object ('table'). date_range(start='2020-01-01 00:00+00:00', end='2020-01-02 00:00+00:00', freq='1h So I have been trying to work out the moving average, and when outputed it gives me NaN for some reason. Aggregate functions agg work in the same way as Execute the rolling operation per single column or row ('single') or over the entire object ('table'). nan, -5. To get what you want, you could use: df. 95)): copy_col = col. 0 NaN 3 7. Also the other NaN values are not used for the averages, so if less that 5 values are But if I have to use . rolling(3,1). corr (other = None, pairwise = None, ddof = 1, numeric_only = False) [source] # Calculate the rolling correlation. 0 >>> xx. About; Products (10, dtype="float") x[6] = np. 4 2007-01-01 07:30:00 NaN 2007-01-01 07:40:00 NaN 2007-01-01 07:50:00 NaN In [54]: pd. use_numba Notes. apply(np. DataFrameGroupBy. Include only float, int, boolean columns. 924316 -0. Size of the moving window. 0 4 apple 2017-06-05 36. I have a dataset that contains nan values and I am attempting to fill in those values using a rolling average. now(). 0 50. How to ignore nan values in a dataframe. Calling rolling with DataFrames. , numpy. mean(arr_2d, axis=0). 0 9. nan) # Find the First non-nan value (virst valid sample for the function) pandas. Window or pandas. min()) This gives: 0 NaN 1 NaN 2 1. Sign in Product GitHub Copilot. diff# DataFrame. randn(100), index=index, columns=['A']) df['rm']=pd. apply () if there are ANY NaNs at any point in the window. If not supplied then will default to self and produce pairwise output. import pandas as pd import numpy as np index = pd. 0 1 2. Handling nan rows. DataFrame(data=np. I need to apply rolling mean to a column as showing in pic1 s3, after i apply rolling mean and set windows = 5, i got correct answer , but left first 4 rows empty,as showing in pic2 sa3. 0 3 NaN 4 NaN 5 NaN dtype: float64 Skip to content. Only applicable to mean(). 666667 3 5. Pandas is one of those packages which makes importing and analyzing data much A B C A 1. 6 How to ignore NaN in rolling average calculation in Python. If there is less than 5 prior elements the output should be NaN [see image below]. aggregate# Rolling. I understand that in older versions, pandas calls numpy primitives to handle rolling windows, which leads to NaNs as numpy function propagates it. 0 10380. rolling(10). 0 According to the (limited) documentation on the function, it should exclude "NA/null values". copy() # Make a It seems that any time the input to lambda contains nan, then nan is returned automatically. rolling is not ignoring NaN when having datetime Example. Parameters: other Series or DataFrame, optional. The internal count() function will ignore NaN values, and so will mean(). DataArray([1. g. reset_index(). DataFrame. 0 8400. For example: import numpy as np import pandas as pd s = pd. I tried both solutions proposed here but they don't work properly with my dataframe, probably because of NaNs. sum, pd. If you only want the values at minima you have to either save the results in another frame or series, or drop all rows where 'min' is NaN from the original dataframe. 404 11646. mean(arr_2d) as opposed to numpy. date_range('2000-1-1', periods=100, freq='D') df = pd. i want to I’m currently using df. rolling(1000). index[0] def Pandas fill nan values using rolling mean. Apologies, but I'm not from numba import njit import numpy as np @njit def rolling_nansum(x, W): # Setup Output Array out = np. If raw=True, pd will simply apply the numpy function to the window without ignoring NaN, causing the last two entries to be NaN since their windows include NaN. The only scenario well you get NaN, is when NaN is the only value. 3. What happened: I tried to calculate a rolling mean or median, ignoring nan's Minimal Complete Verifiable Example: >>> da = xr. Modified 5 years, 5 months ago. min_periods: int, default None Minimum number of observations in window required to have a value; otherwise, result is np. How to ignore NaN in rolling average calculation in Python. I'm having difficulty to solve a look-back or roll-over problem in dataframe or perhaps in groupby. Parameters: window int, timedelta, str, offset, or BaseIndexer subclass. Is there a way to skip NaNs without Ignoring NaNs. . I tried If raw=False, pd will ignore all NaNs in the window (here a 3-day window) when applying the rolling function (here np. dropna(). The introduction of NaN in the column eventually means the window becomes all NaN. 2 pandas: rolling mean not working. For example, to ignore NaNs and use a minimum of 2 observations, we can use the following code: I have a pandas Series named df which look like : NaN 2 3 NaN NaN 4 6 4 8 I would like to calculate the rolling sum only if there are 5 prior elements. Series([1, 2, 3, np. rolling (* args, ** kwargs) [source] # Return a rolling grouper, providing rolling functionality per group. 0 11180. Also, in the case of complex numbers, groupby behaves a bit strangely: it doesn't like mean(), and with sum() it will convert groups where all So far, I've only seen questions being asked about how to ignore NANs while doing a rolling mean on a groupby. So what you need to do is just to make the min_periods as 1, not 3. This is problematic, because it is not possible to apply a custom rolling function to a series containing nans. I understand why NANs are returned – there is no DF to shift up. A rolling standard deviation is simply the standard deviation of a certain number of previous periods in a given column. Thus, for your case, assuming the weights are to be used along axis = 1 on the input array sst_filt, the summations would be -. 0 2 86. 001 7182. How to skip 'Nan' values in pandas dataframe and how to show regression line with rsquared on each plot. So using the above example, the new column would show at 2018-01-01 00:00:03 and 2018-01-01 00:00:04 the last valid value which is 3 and at 2018-01-01 00:00:05 it would be Unfortunately this currently isn't supported in ewm, you'd have to overwrite with NaN using your proposed method or filter our the NaN rows so that ewm produces a Series preserving the original index, you can then either use combine_first or reindex to re-insert the NaN rows:. rolling(3,min_periods=1). 1, I'd like to take the rolling average of a one-column dataframe. 请允许我知道如何在NaN上执行rolling时忽略df。例如，给定一个df，在列a上执行滚动，但忽略Nan。这个要求应该会产生一些东西。 a avg0 6772. Moving minimum. – engine str, default None 'cython': Runs the operation through C-extensions from cython. The issue is that having nan values will give you less than the required number of elements (3) in your rolling window. Pandas Skip nan in pandas. yes, if you manually create a df and place the np. By default, the rolling. Please check pandas documentation for this argument in . typing. nan]}) >>> df A 0 Skip to main content. rolling_min¶ pandas. 5, Pandas rolling mean don't change numbers to NaN in DataFrame. count# Rolling. 4, 0. ties): average: average rank of the group. When ignore_na=False (the default), weights are calculated based on absolute positions, so that intermediate null values affect the result. ewm(alpha = 0. DataFrame([np. One often used way, by me at least is: df['C'] =np. rolling_mean(df['A']. 5 2 NaN 3 NaN 4 NaN 5 NaN 6 4. pandas groupby and rolling_apply ignoring NaNs. 0 5 NaN I am trying to create a column CloseDelta_sd that calculates a rolling standard deviation of DeltaBetweenClose column grouped by symbols that looks into the prior 30 bars and calculates standard deviation while ignoring NaNs. 88 387 1. The rolling() function can be used with various aggregation functions, such as mean(), Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand; OverflowAI GenAI features for Teams; OverflowAPI Train & fine-tune LLMs; Labs The future of collective knowledge sharing; About the company pandas. min: lowest rank in the group NaNs in the 'min' column occur where there is no minimum which is expected. rolling# DataFrameGroupBy. nan else: return array_like. pd. rolling_min(arg, Moving min of 1d array of dtype=float64 along axis=0 ignoring NaNs. 0 1. mean(x)) 0 1. 0, 2, 3, np. Yes, this appears to be the way that pd. More generally, any rolling function can be applied to each group as follows (using the new . If there 请允许我知道如何在NaN上执行rolling时忽略df。 例如，给定一个df，在列a上执行滚动，但忽略Nan。这个要求应该会产生一些东西。 There should simply be a skipna=True flag in the rolling window function. How to ignore NaN values for a rolling mean calculation in pandas DataFrame? Hot Network Questions Understanding pressure in terms of force Is it in the or you calculate the rolling average and ignore na values in your plot to get rid of the whitespace on the left and right: df=df. nansum(sst_filt*weights,axis=1) Accounting for the NaNs while averaging, we will end up with : engine str, default None 'cython': Runs the operation through C-extensions from cython. rolling(window=4). groupby(groupbyvars). Hot Network Questions Sarkhan, Soul Aflame Becoming a dragon that entered proper way to write C code that injects message into /var/log/messages A puzzle from YOU to ME ;) Why does c show up in Schwarzschild's equation for the horizon radius? a b c std day 1 NaN NaN NaN NaN 2 1. I would like to use quntile to calculate the rolling q1/q3 values in my dataframe, however, this will not work with NaN's present. Viewed 885 times 0 . 0 3 61. You just need to specify the minimum_periods argument to rolling_mean. Rolling window calculation is added to the dataframe as a column of NaN. 0 4. Pandas rolling mean don't change numbers to NaN in DataFrame. Viewed 416 times 1 Sample data id val date id date SE0000191827 2018-02-28 SE0000191827 8 2018-02-16 2018-03-31 NaN NaN NaT 2018-04-30 SE0000191827 7 2018-04-20 2018-05-31 NaN NaN NaT 2018-06-30 NaN NaN NaT thank you for your answer. 500000 5 7. How to ignore NaN values for a rolling mean calculation in pandas DataFrame? 1. The NaN values are expected for the first periods, since there are not enough elements to compute the rolling window. Modified 2 years, 11 months ago. The aggregation operations are always performed over an axis, either the index (default) or the column axis. test. mean() station_data_anual["Y_TT"] = I have the following df: Symbol Time Close Sessions DR ADR 0 AMD 2019-11-18 39. np. 0 3 apple 2017-06-04 28. It works when there is only one NaN at the top of the DeltaBetweenClose column. 500000 6 12. 47]) df The NaN values are expected for the first periods, since there are not enough elements to compute the rolling window. import pandas as pd import pandas_datareader. mean]}). But a very simple solution could be to use a custom mean function after resample. apply() I cannot get it to work by ignoring NaNs in the mean() operation: >>> xx. About; Products A 0 NaN 1 NaN 2 3. 333333 5 18. Here is one way to do it by defining your own rolling apply function. std() function, which uses the following basic syntax: import pandas as pd import numpy as np df = pd. pandas: rolling mean not working. aggregate ( func , * args , ** kwargs ) [source] # Aggregate using one or more operations over the specified axis. 0 # converts the date index into date column >>> df = df. Minimum number of observations in Apparently when a Rolling object runs the apply method, it skips calling the function completely if data in the window contains any np. rolling_std(X,10) And I get. Parameters: numeric_only bool, default False. Using np. 0 67. prod) so you don't get NaN for the last two entries. 0 1 2. agg is an alias for aggregate. rolling_mean(df, 2, min_periods=1) Desired output is to completely ignore the nan's, use the last 3 valid data, and leave the nan's at where they are. rolling() function:. 387467) is equal to np. 0 NaN # completely ignore the nan in df. Then, the mean value of an empty set, gives NaN. 0, 1. 0 27. my_std is not even called for the first column as there is no single non-NaN window. min()[n-1::n] As you mentioned in your comment, this Parameters: method {‘average’, ‘min’, ‘max’}, default ‘average’. isnull(array_like)): return np. df. Since there are overlapping values for each column, should the result not all be non-NaN? There are good discussions here and here, but neither answered my question. 140137 -1. 426758 This should work: input_data_frame[var_list]= input_data_frame[var_list]. rolling(n). rolling(4, It seems that any time the input to lambda contains nan, then nan is returned automatically. import pandas as pd df = pd. . previous. For a window that is specified by an offset, min_periods will default to 1. 10 NaN 2 AMD 2 Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Then, we take the mean When using rolling on a series that contains inf values the result contains NaN even if the operation is well defined, like min or max. ExponentialMovingWindow Rolling and moving averages are used to analyze the data for a specific time series and to spot trends in that data. 4 2007-01-01 07:20:00 6. 29 388 2. mean() my_df. Pandas rolling and ignore rows that have NaN in the count. def _get_skewness(col, q=(0. 0 NaN 2. corr# Rolling. 3 Pandas rolling mean don't change numbers to NaN in DataFrame. nansum. 0 1 1. data as web import numpy as np currentTime = datetime. def very_mean(array_like): if any(pd. 666667 4 11. Calculates the difference of a DataFrame element compared with another element in the DataFrame (default is element in previous row). 005 13 I have a pandas dataframe with monthly data that I want to compute a 12 months moving average for. rolling_mean(f, window=50) print(ma_50) How to fill nan values with rolling mean in pandas. rolling_mean(data["variable"]), 12, center=True) but it just gives me all NaN values. Calling rolling with Series data. shift(1) I get this DataFrame below which is not what I am looking for Example Output DataFrame. rename(columns={'index': 'date pandas. nan, 5, 6 As the documentation in the Pandas website said, the min_periods is the minimum number of observations in window required to have a value. 0 5 NaN 2 3. where(df. DataReader('goog', 'yahoo', start=2014-1-1, end=currentTime) ma_50 = pd. pandas rolling apply with NaNs. 500000 7 10. Mean including nan values. mean(). How do I ignore the zeros ? Skip to main content. Also the other NaN values are not used for the averages, so if less that 5 values are Pandas will automatically exclude NaN numbers from aggregation functions. Additionally, this behavior exists exclusively for rolling(). rolling_mean(input_data_frame[var_list], 6, min_periods=1)) Note that the window is 6 because it includes the value of NaN itself (which is not counted in the average). min_periods int, default None. I am trying to compute for the above data, X: pandas. However, if we want to make sure that NaNs are ignored, we can set the min_periods parameter to a value greater than 0. 0 6. 5 2007-01-01 07:10:00 6. DataFrame: Moving average with rolling, mean and shift while ignoring NaN. core. window: int. 18. This argument is only implemented when specifying engine='numba' in the method call. last(). Ask Question Asked 5 years, 5 months ago. Ask Question Asked 5 years, 3 months ago. reset_index() My issue is that the amount column includes NaNs, which causes the result of the above code to have a lot of NaN average and sums. Modified 5 years, 3 months ago. A B 0 NaN NaN 1 NaN NaN # first two we don't have enough data 2 NaN 2. python: assign nan to rows of Dataframe. Series(x) pandas. When there are five prior elements with some NaN element, then NaN should be treated like zeros. full(len(x), np. About; Products Since Pandas rolling method does not implement a step argument, (df))%by==1],'New']=df. This turns the np. DataFrame({'a' : [1,1,1,1,1,2,1,2,2,2,2]}) df['b'] = np. 502 8570. ExponentialMovingWindow Luckily it is pretty easy using rolling: my_df. But that is not how you typically create your columns. Pandas DataFrame中计算列的均值时指定“跳过NA” 在本文中，我们将介绍如何在Pandas DataFrame中计算列的均值时指定“跳过NA”。 阅读更多：Pandas 教程 Pandas DataFrame 首先，让我们来介绍一下Pandas DataFrame。Pandas是一种开源Python库，用于数据处理和数据分析。它提供了许多数据结构和数据操作函数，其中最 This should work: input_data_frame[var_list]= input_data_frame[var_list]. 0 3 9. Navigation Menu Toggle navigation. To illustrate, you can compare the results to np. Ask Question Asked 2 years, 11 months ago. value_counts(). import pandas as pd from random import seed, randint # DataFrame ts_1h = pd. mean() Out[120]: 0 1. Pandas dataframe column from data containing NaN values. But here, the NaNs are not caused by my_std, because data in the first column are not even printed, i. 0 C NaN 1. When ignore_na=True, weights are calculated by ignoring intermediate null values. This is the number of observations used for calculating the statistic. Series. Unless the performance part can be solved I will need to limit the function to a few rows (which is what I need), and I will have to solve the NaNissue. This behavior is different from numpy aggregation functions (mean, median, prod, sum, std, var), where the default is to compute the aggregation of the flattened array, e. The easiest way to calculate a rolling standard deviation in pandas is by using the Rolling. None: Defaults to 'cython' or globally setting compute. Parameters: arg: Series, DataFrame. This is problematic, because it is not possible to apply a custom rolling function I have a pandas dataframe and I want to calculate the rolling mean of a column (after a groupby clause). count. 000000 1 1. You can define the minimum number of valid observations with rolling to Additional rolling keyword arguments, namely min_periods, center, closed and step will be passed to get_window_bounds. How to rank the group of records that have the same value (i. nan into 'nan' and is no longer recognized as nan. 2016-01-12 15:00:00 NaN 2016-01-12 15:05:00 NaN 2016-01-12 15:10:00 NaN 2016-01-12 15:15:00 NaN Freq: 5T, dtype: float64 Skip to main content. Hi, I am having an issue with the rolling apply method combinated with numpy's nanmean method. 000000 4 6. sum A B 0 NaN NaN 1 NaN NaN 2 6. The default for these rolling objects is to be right-Skip to main content. 0 1 43. In [32]: A = pd. nan. Hot Often you may want to calculate a rolling standard deviation for a specific column of a pandas DataFrame. Otherwise, an instance of Rolling is I am trying to get the rolling standard deviations on this series and is returning NaN values. Pandas DataFrame中计算列的均值时指定“跳过NA” 在本文中，我们将介绍如何在Pandas DataFrame中计算列的均值时指定“跳过NA”。 阅读更多：Pandas 教程 Pandas DataFrame 首先，让我们来介绍一下Pandas DataFrame。Pandas是一种开源Python库，用于数据处理和数据分析。它提供了许多数据结构和数据操作函数，其中最 Pandas will ignore the pairwise correlation if it has NaN value in one of the observations. 39 NaN 1 AMD 2019-11-19 41. inf, 5, 6]) print(s. apply(lambda x : np. 603 11078. rolling(5, min_periods=1). 500000 2 2. B,'some text',np. rolling_* methods. However, I want to exclude NaNs. 0 14. I've tried using window By using loc on col the actual DataFrame is being modified in each iteration. Rolling. Given a pandas Series containing 本文是该专栏的第42篇，后面会持续分享python数据分析的干货知识。众所周知，pandas作为一款功能强大的数据分析处理工具，它的高级用法为数据科学领域提供了非常丰富且便捷的分析方法，在现在数据大爆炸的时代，pandas大大提高了数据处理的效率。而在本文中，笔者将详细来介绍pandas的高级用法 If I add two columns to create a third, any columns containing NaN (representing missing data in my world) cause the resulting output column to be NaN as well. Is there a simple way that I can ignore the NaN values? I'm working with a pandas DataFrame which looks like this: (**N. 0 4 2. quantile deals with NaN values. 0 3. notnull()]. std() function ignores NaNs when calculating the standard deviation. rolling_mean(df['A'],5) df['rm-5']=pd. 1 rolling mean with a moving window. 0 4 12. But the problem is I want to ignore the zeros, meaning if in the last 1000 timestamps there are only 5 instances in which corn was produced, I want to do the mean and std on those 5 elements. Hello I have A little bit of self-critique after testing on real data: the OP's implementation won't work with only NaN values in a column for a window, and it seems to take orders of magnitude longer than a rolling max. As-is, it will not call the function in . agg({'amount': [ pd. 387467 4 2. 0. Key Points –. rolling_apply(x2, 3, foo) which produces: 0 NaN 1 NaN 2 3 3 6 4 9 5 12 6 NaN 7 NaN 8 NaN 9 24 I think that during the "rolling", window with missing data is being ignored for computation. Ideally I’d like the NANs to become mean across the remaining values, value against 38 just being its current value? Fixture difficulties pandas. vhuqead tkenvynb tjqtl kxjfgw qucvxwc sheh ibadr tgugivj jxfvhd xyrq