last_valid_index Return index for last non-NA value or None, if no non-NA value is found. Uproot has a limited ability to write ROOT files, including TTrees of Although you see something that looks like a JaggedArray, the type However, some types are not fully split by ROOT and have to be Return int position of the smallest value in the Series. Localize tz-naive index of a Series or DataFrame to target time zone. Fill NaN values using an interpolation method. # [0.06317138671875 -0.20782470703125 -11.5118408203125] # [0.00823974609375 -0.0311279296875 -11.839599609375], # [-0.09063720703125 -0.047607421875 -10.6365966796875], # [0.010986328125 -0.2984619140625 0.7855224609375]], # [[0.113525390625 -0.07232666015625 4.2095947265625], # [-0.142822265625 0.205078125 8.75152587890625]. (DEPRECATED) The week ordinal of the year according to the ISO 8601 standard. Doubly nested JaggedArrays are a native type in Awkward Array: they # array([20.28261757, 20.47114182, 20.5931778 , 20.5848484 , 20.80287933. Histograms can be written to the file in the same way: by assignment 0. 7. categorical variable; instead of counting unique Get the Timestamp for the start of the period. to see how to put Numpy arrays to work in machine learning. message. Returns all column names and their data types as a list. pandas.Series.value_counts# Series. Returns a DataFrameStatFunctions for statistic functions. Return the bool of a single element Series or DataFrame. structure, and Numpy The purpose of most of these methods is to extract data, which includes Indicate whether the date is the first day of a year. 10. get the latest one. Series.bfill(*[,axis,inplace,limit,downcast]), Series.dropna(*[,axis,inplace,how]). Compute covariance with Series, excluding missing values. ? creating the tree. Series.rtruediv(other[,level,fill_value,axis]), Series.rfloordiv(other[,level,fill_value,]). Series.max([axis,skipna,level,numeric_only]). false. provide quick and easy access to pandas data structures across a wide range of use cases. It will be applied to each column in by independently. If nothing happens, download GitHub Desktop and try again. Another option, of course, is to use a batch system (Condor, Slurm, To use a lazy array as a window elements on its own when memory is scarce. Draw histogram of the input series using matplotlib. use the maximum per file: numpy.inf. # [16, 16, 16, 16, 16, 16, 16, 16, 16, 16]. # fTracks.fMass2 TStreamerBasicType asjagged(asfloat16(0.0, 0.0, 8, # dtype([('exponent', 'u1'), ('mantissa', '>u2')]), dtype('float32'))). 8. high-performance arrays that are only turned into objects when you look them. difference between files and may be used as a TChain alternative. Series.rtruediv(other[,level,fill_value,axis]), Series.rfloordiv(other[,level,fill_value,]). Return lowest indexes in each string in Series/Index. 12. Return sample standard deviation over requested axis. Fixed-width arrays are exploded into one column per element when viewed [see GH5390 and GH5597 for background discussion.]. # 32.67634359, 32.70165023, 168.78012134, 81.27013558, "http://scikit-hep.org/uproot3/examples/foriter.root", # (entrystart, entrystop) pairs where ALL the TBranches' TBaskets align, # [(0, 6), (6, 12), (12, 18), (18, 24), (24, 30), (30, 36), (36, 42), (42, 46)]. between uproot3.asobj Series.reset_index([level,drop,name,]). In the example below, Track objects under fTracks have been # b'py1': array([ 17.4332439, -16.5703623, -16.5703623, , 1.1994057, 1.199405, 1.2013503]), # b'pz1': array([-68.9649618, -48.7752465, -48.7752465, , -74.5324306, -74.532430, -74.8083724])}. Get the properties associated with this pandas object. Return Exponential power of series and other, element-wise (binary operator rpow). Determine if each string starts with a match of a regular expression. Unfortunately, the same does not apply to doubly nested jagged arrays, is ObjectArray, meaning that you only have some bytes with an Get the Timestamp for the end of the period. Return Equal to of series and other, element-wise (binary operator eq). array that it has previously read. 10. Return an array of native datetime.timedelta objects. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. The object is now in the file. 0. Return the maximum of the values over the requested axis. (I get notified of questions with this tag.) Apply the key function to the values before sorting. For numeric data, the results index will include count, mean, std, min, max as well as lower, 50 and upper percentiles. Return the row label of the maximum value. 0. # [ 0., 3., 14., 50., 90., 86., 58., 23., 4., 1.]. Access a single value for a row/column pair by integer position. Return Integer division of series and other, element-wise (binary operator rfloordiv). Series.interpolate([method,axis,limit,]). Consider, for TBranches and TLeaves have no Convert strings in the Series/Index to be capitalized. Pivot tables#. We have already seen that TBranches can be selected as lists of strings 0. Observe (named) metrics through an Observation instance. as jagged arrays (of ROOTs Float16_t encoding). stats.mode returns a tuple of two arrays, so you have to take the first element of the first array in this tuple. Return cumulative sum over a DataFrame or Series axis. These functions let you make a lazy array that spans many files. Its unlikely that youd ever want to do that, unless the 0. Series.rmul(other[,level,fill_value,axis]). Access a single value for a row/column label pair. This is because a function that returns objects selects branches and 13. compressed with LZMA, or compressed with LZ4. This is an example of how you would add a title to your tree: To specify the title of the branch, similar to how you would add a title explicitly pip-installed.). be less than the target size. (integers and floating-point numbers). Python. 10. Return Exponential power of series and other, element-wise (binary operator pow). processing that had to be done was to find out how many entries each Series.fillna([value,method,axis,]). Replace each occurrence of pattern/regex in the Series/Index. ]), # . Set the name of the axis for the index or columns. Return unbiased variance over requested axis. Combine the Series with a Series or scalar according to func. Appropriate translation of "puer territus pedes nudos aspicit"? # [[ 1.1283927 , 1.20095801, 0.7379719 , 0. You can also specify the compression of each branch individually by using the uproot3.newbranch() method. Return Series/DataFrame with requested index / column level(s) removed. # . Maps an iterator of batches in the current DataFrame using a Python native function that takes and outputs a PyArrows RecordBatch, and returns the result as a DataFrame. Modify Series in place using values from passed Series. Write records stored in a DataFrame to a SQL database. 12. # [ 1., 2., 8., 22., 42., 42., 25., 9., 2., 0.]. Connect and share knowledge within a single location that is structured and easy to search. Return an xarray object from the pandas object. Series.drop([labels,axis,index,columns,]). computation with lazy array and dataframe interfaces. Set the parameter n= equal to the number of rows you want. or as jagged arrays of fixed arrays (of ROOTs Double32_t encoding). wont help. cache only needs to behave like a dict (many third-party Python One-dimensional ndarray with axis labels (including time series). Series.rdiv(other[,level,fill_value,axis]). But it is particularly useful that Uproot recognizes Numpy Boolean indicator if the date belongs to a leap year. Group Series using a mapper or by a Series of columns. than bytestrings.) (The new version of Uproot was motivated by the new version of Awkward, to make a clear distinction.). Series.clip([lower,upper,axis,inplace]), Series.corr(other[,method,min_periods]). align (other [q, axis, numeric_only, accuracy]) Return value at the given quantile. Series.attrs is a dictionary for storing global metadata for this Series. Fill NA/NaN values using the specified method. To select elements of inner lists (Pandass These functions also have a basketcache particles): And Numpy arrays of booleans select from outer lists (i.e. These array-reading functions have many parameters, but most of them If youre struggling with a script that takes a long time with the same name, they will have different cycle numbers, with the It differs from installed, youll get an error with installation instructions in the introduction Example: Which is clearly wrong (see the A value that should be 1 and not 4) because it can't handle with unique values. We can specify the title, the flushsize and the compression while Rearrange index levels using input order. Return the integer indices that would sort the Series values. a dict-like interface, the object need not have a name; only the lookup Series.resample(rule[,axis,closed,label,]), Series.tz_convert(tz[,axis,level,copy]). Return Greater than or equal to of series and other, element-wise (binary operator ge). special features below. Series.min([axis,skipna,level,numeric_only]). Note that this tag is primarily intended for the new version of Uproot, so if you're using this version (Uproot 3.x), be sure to mention that. Return cumulative maximum over a DataFrame or Series axis. Axis for the function to be applied on. These arrays can be saved to files in a Series.fillna([value,method,axis,]). Return the frequency object for this PeriodArray. Check whether all characters in each string are numeric. 10. Render object to a LaTeX tabular, longtable, or nested table. Return Series as ndarray or ndarray-like depending on the dtype. # 20.2972393 , 20.30301666, 20.87490845, 20.56552505, 20.67128181. Check whether all characters in each string are alphabetic. Returns a new DataFrame by adding a column or replacing the existing column that has the same name. # [ 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]. at individuals. specified by ROOTs internal baskets (specifically, the places where the Return a Series/DataFrame with absolute numeric value of each element. Return Addition of series and other, element-wise (binary operator radd). Return boolean Series equivalent to left <= series <= right. Convert Series from DatetimeIndex to PeriodIndex. 10. Return whether all elements are True, potentially over an axis. Please You can get the mode by using the pandas series mode() function. along a Dataframe axis that is True or equivalent (e.g. DataFrames don't have a. 8. then use only boolean data. impression that you have a larger array than memory can hold all at Indicate whether the date is the last day of the year. (Note: only ufuncs recognize these lazy arrays because Numpy 9. middle of a basket (see below). arraysbecause arrays dont have an index to record that informationbut properties other than their names. In this view, many of the attributes are not special classes and can arraysChunkedArray and VirtualArraywhich are not Numpy Series.groupby([by,axis,level,as_index,]). Series.str.contains(pat[,case,flags,na,]). choose n distinct combinations of elements per event. Since theyre not names, theres no Return Equal to of series and other, element-wise (binary operator eq). You cant apply the Aggregate using one or more operations over the specified axis. encouraged to contribute them to it has enough elements to hold the (possibly type-converted) output. Series.sparse.from_coo(A[,dense_index]). encoding, such as Latin-1 or Unicode, so Uproot presents them as raw parallelizing that work in many threads has limited benefit because array is managed by an external system. I have a data frame with three string columns. The dtype of each result column is always object, even when no match is found. and must include the cycle number. root_numpy reads data like Pandas doesnt have specialized manage the iteration, as in this histogram accumulation. documentation for details. # 0.30728292, 0.30681205, 0.341563 , 0.16150808, 0. I know that the only one value in the 3rd column is valid for every combination of the first two. key callable, optional. # [-1.16029346, 2.012362 , 4.02206421, 0. It would be nice if pandas provided version of apply() where the user's function is able to access one or more values from the previous row as part of its calculation or at least return a value that is then passed 'to itself' on the next iteration. Get item from object for given key (ex: DataFrame column). Return boolean if values in the object are unique. The lack of per-event Whereas Convert time series to specified frequency. graphs. Cast a pandas object to a specified dtype dtype. # [ 1.22933233, 1.39499295, 2.17524433, 0. These arrays can be used with Numpys universal may have non-trivial shape and dtype Determine if each string entirely matches a regular expression. the DataFrame index. memory unit: B for bytes, kB for kilobytes, MB, GB, and A NumPy ndarray representing the values in this Series or Index. Objects in ROOT files can be uncompressed, compressed with ZLIB, pandas provides dtype-specific methods under various accessors. Convert strings in the Series/Index to lowercase. Shift index by desired number of periods with an optional time freq. You can still use the TLorentzVectorArray Python class; you just Series.attrs is a dictionary for storing global metadata for this Series. Returns a new DataFrame that has exactly numPartitions partitions. Series.ge(other[,level,fill_value,axis]). 13. possibilities. Series.mask(cond[,other,inplace,axis,]). Round each value in a Series to the given number of decimals. Return the last row(s) without any NaNs before where. (ufuncs), which are the mathematical functions that perform elementwise Map all characters in the string through the given mapping table. processing is why reading in Uproot and processing data with Series.median([axis,skipna,level,]). Series.between_time(start_time,end_time[,]). Just as reading behaves like getting particular level, collapsing into a Series. # fTracks.fZfirst TStreamerBasicType asjagged(asfloat16(0, 0, 12. Series.to_sql(name,con[,schema,]). Select final periods of time series data based on a date offset. What can I do fix it? How do I get the row count of a Pandas DataFrame? Flattening turns multiple values per entry (i.e. 12. # . backports.lzma). But remember to other. TTrees are special objects in ROOT files: they contain most of the datetimelike and return several properties. Apply chainable functions that expect Series or DataFrames. library installed with Uproot. 0. For exploratory work or to control memory usage, you might want Convert strings in the Series/Index to lowercase. What happens if you score more than 99 points in volleyball? By default the lower percentile is 25 and the upper percentile is 75.The 50 percentile is the same as the median.. For object data (e.g. DataFrames like the above are slow (the cell entries are Python lists) into a very large dataset, youll have to limit how much its allowed to not defined in Uproot (which is strictly concerned with I/O), but in so they behave like Python dicts, too. Series.rpow(other[,level,fill_value,axis]). ]. # {'http://scikit-hep.org/uproot3/examples/sample-5.23.02-zlib.root': 30. types, and TObjString (for metadata). It has histogram I know that the only one value in the 3rd column is valid for every combination of the first two. Series.rmod(other[,level,fill_value,axis]). Series.radd(other[,level,fill_value,axis]). and TLeaf is the data type descriptor. 0. Numpy has no analogue for this type. Rearrange index levels using input order. # -1.8, -1.6, -1.4, -1.2, -1. , -0.8, -0.6, -0.4, -0.2, 0. , 0.2. 12. As with all ROOT object names, the TBranch names are bytestrings ]. Rsidence officielle des rois de France, le chteau de Versailles et ses jardins comptent parmi les plus illustres monuments du patrimoine mondial et constituent la plus complte ralisation de lart franais du XVIIe sicle. There's also an additional solution that supports multiple modes. If you have a small, fast disk, you may want to consider True, then the result will be False, as for an empty row/column. shape Get item from object for given key (ex: DataFrame column). assumes that all the branches have equal number of baskets and will not demand) to the same Python string type. If level is specified, then, DataFrame is returned; otherwise, Series Series.to_pickle(path[,compression,]), Series.to_csv([path_or_buf,sep,na_rep,]). and TBranchMethods, but theres also module-level uproot.lazyarray and uproot.lazyarrays. The next two methods explicitly step through chunks of data, to Datastores are attached to workspaces and are used to store connection information to Azure storage services so you can refer to them by name and don't need to remember the connection information and secret used to connect to the storage services. Check whether all characters in each string are digits. use to match file lists: * can be replaced with any text (or none), 8. ]. # 'http://scikit-hep.org/uproot3/examples/sample-6.08.04-zlib.root': 30. 9. In the simplest case, the except that you dont have to specify the TBranch name (naturally). Awkward Array generalizes Numpy in many waysdetails can be found in Return the number of bytes in the underlying data. be read as arrays of numbers. # {'px1': array([-41.1952876, 35.1180497, 35.11804977, , 32.377491, 32.377491, 32.485393]). # array([12, 13, 14, 15, 16, 17], dtype=int32). Series.iat. to manipulate that structure. Internally, ROOT files are written in chunks and whole chunks must be fills and returns that array. 11. Series.mode also does a good job when there are multiple modes: Or, if you want a separate row for each mode, you can use GroupBy.apply: If you don't care which mode is returned as long as it's either one of them, then you will need a lambda that calls mode and extracts the first result. These are separate namespaces within Series that only apply Categorical-dtype specific methods and attributes are available under it does in other array-reading functions, but its effect would be to 1980s short story - disease of self absorption, TypeError: unsupported operand type(s) for *: 'IntVar' and 'float'. 0. mean (axis = _NoDefault.no_default, skipna = True, level = None, numeric_only = None, ** kwargs) [source] # Return the mean of the values over the requested axis. 0. Series.str.extract(pat[,flags,expand]). quotation marks) will be interpreted as regular expressions instead The percent of non- fill_value points, as decimal. For this, Uproot fills a new JaggedArray data structure (from the # [ 0.06950195, 0.79105824, 2.0322361 , 0. # [ 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]. (DEPRECATED) Equivalent to shift without copying data. # [ 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]. as a Find indices where elements should be inserted to maintain order. histogram of particles, ignoring event boundaries), functions like numpy.histogram require non-jagged arrays, so flatten them with a call to .flatten(). Suppose I have a 5*3 data frame in which third column contains missing value. Write object to a comma-separated values (csv) file. Return a new DataFrame with duplicate rows removed, optionally only considering certain columns. archivedthis How to smoothen the round border of a created buffer to make it look more natural? own C++ classes, Uproot should be able to read them. Combine the Series with a Series or scalar according to func. Python does not necessarily mean slow. ]). 0. Series.convert_dtypes([infer_objects,]). lets us unpack the arrays in Pythons for statement. (prepended by b). Series.mean([axis,skipna,level,numeric_only]). Split the string at the last occurrence of sep. Slice substrings from each element in the Series or Index. Count occurrences of pattern in each string of the Series/Index. the date is was recorded, the URL it was accessed from, etc.) dtype. (Theres one more edge than contents to cover left and right.). There is also a keycache for caching Series.to_json([path_or_buf,orient,]). This string consists of a number followed by a Select values between particular times of the day (e.g., 9:00-9:30 AM). 0. (corresponding to ROOTs "CREATE", "RECREATE", and "UPDATE" Number of nanoseconds (>= 0 and less than 1 microsecond) for each element. important enough to be given convenience methods for analysis. This is similar to the key argument in the builtin sorted() function, with the notable difference that this key function should be vectorized.It should expect a Series and return a Series with the same shape as the input. The day of the week with Monday=0, Sunday=6. Returns a DataFrameNaFunctions for handling missing values. Prefix labels with string prefix.. add_suffix (suffix). has split the objects. Whereas cachetools The flatname parameter determines how fixed-width arrays and field (DEPRECATED) Return boolean if values in the object are monotonically increasing. Assume there are 2 branches in the TTree: The extend method takes a dictionary where the key is the name of the should be stored running out of memory during iteration, try reducing the entrysteps. 14. Series.le(other[,level,fill_value,axis]). When you write objects to the ROOT file, they can be unnamed things like Series.str.contains(pat[,case,flags,na,]). Series.mask(cond[,other,inplace,axis,]). {0 or index, 1 or columns, None}, default 0. For consistencys sake, the # (array([82.20186639, 62.34492895, 62.34492895, , 81.27013558, 81.27013558, 81.56621735]), # array([82.20186639, 62.34492895, 62.34492895, , 81.27013558, 81.27013558, 81.56621735])). Projects a set of SQL expressions and returns a new DataFrame. a Python string, but they get stamped with the lookup name once they In physics data, it is even more common to have an arbitrary number of This causes the whole # -0.0455109 , 0.72099614, 1.48750319, 2.25401024, 3.02051729. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. pandas.DataFrame.mode pandas.DataFrame.pct_change pandas.DataFrame.prod ints is given every integers corresponds with one column. Parameters axis {index (0), columns (1)} Axis for the function to be applied on. ZLIB is part of the Python Standard Library, and LZMA is part of crosses file boundaries as part of its iteration, and thats information (pandas.MultiIndex), Series.radd(other[,level,fill_value,axis]). 12. Return index for last non-NA value or None, if no non-NA value is found. 16.]. Returns the schema of this DataFrame as a pyspark.sql.types.StructType. Each of those is a standard histogram object, something that would Create a multi-dimensional rollup for the current DataFrame using the specified columns, so we can run aggregation on them. Return a new DataFrame containing union of rows in this and another DataFrame. Convert tz-aware Datetime Array/Index from one time zone to another. 0. Duplicate each string in the Series or Index. Returns True if the collect() and take() methods can be run locally (without any Spark executors). Return Series with specified index labels removed. . 13. Series.mean([axis,skipna,level,numeric_only]). Suffix labels with string suffix.. agg ([func, axis]). 0. In python, how can I reference previous row and calculate something against it? 14. A function is a block of code which only runs when it is called. 0. Render a string representation of the Series. Whether each column contains at least one True element (the default). 0. Localize tz-naive index of a Series or DataFrame to target time zone. Here is an example of JaggedArrays in physics data: Note that if you want to histogram the inner contents of these arrays (i.e. Access a single value for a row/column label pair. Series.sum([axis,skipna,level,]). shortcut method (which reads less data than normal file-opening): By default, lazy arrays hold onto all data that have been read as long is less useful than it is with lazy arrays. iterating. or anything.). at those keys: If youre running out of memory, you could manually clear your cache by 0. Series.notnull is an alias for Series.notna. anything that Python evaluates to true or 0. Return Subtraction of series and other, element-wise (binary operator rsub). 0. Series.kurtosis([axis,skipna,level,]). If skipna is False, then NA are treated as True, because these are not Return the elements in the given positional indices along an axis. 0. Return DataFrame with labels on given axis omitted where (all or any) data are missing. ]))]), "http://scikit-hep.org/uproot3/examples/histograms.root", # 0 2410.8, # +------------------------------------------------------------+, # [-inf, -3) 0 | |, # [-3, -2.4) 68 |** |, # [-2.4, -1.8) 285 |******* |, # [-1.8, -1.2) 755 |******************* |, # [-1.2, -0.6) 1580 |*************************************** |, # [-0.6, 0) 2296 |********************************************************* |, # [0, 0.6) 2286 |********************************************************* |, # [0.6, 1.2) 1570 |*************************************** |, # [1.2, 1.8) 795 |******************** |, # [1.8, 2.4) 289 |******* |, # [2.4, 3) 76 |** |, # [3, inf] 0 | |, # 0 0.24108, # +----------------------------------------------------------+, # [-inf, -3) 0 | |, # [-3, -2.4) 0.0068 |** |, # [-2.4, -1.8) 0.0285 |******* |, # [-1.8, -1.2) 0.0755 |****************** |, # [-1.2, -0.6) 0.158 |************************************** |, # [-0.6, 0) 0.2296 |******************************************************* |, # [0, 0.6) 0.2286 |******************************************************* |, # [0.6, 1.2) 0.157 |************************************** |, # [1.2, 1.8) 0.0795 |******************* |, # [1.8, 2.4) 0.0289 |******* |, # [2.4, 3) 0.0076 |** |, # [3, inf] 0 | |, # 0 1.05, # +--------------------------------------------------------+, # [-inf, -5) 0 | |, # [-5, -4.1667) 1 |***************************************************** |, # [-4.1667, -3.3333) 1 |***************************************************** |, # [-3.3333, -2.5) 1 |***************************************************** |, # [-2.5, -1.6667) 1 |***************************************************** |, # [-1.6667, -0.83333) 1 |***************************************************** |, # [-0.83333, 0) 1 |***************************************************** |, # [0, 0.83333) 1 |***************************************************** |, # [0.83333, 1.6667) 1 |***************************************************** |, # [1.6667, 2.5) 1 |***************************************************** |, # [2.5, 3.3333) 1 |***************************************************** |, # [5, inf] 0 | |, # 0 2993.6, # +-----------------------------------------------------+, # [-inf, -3.6179) 0 | |, # [-3.6179, -2.8738) 22 | |, # [-2.8738, -2.1296) 127 |** |, # [-2.1296, -1.3854) 632 |*********** |, # [-1.3854, -0.64124) 1814 |******************************** |, # [-0.64124, 0.10294) 2851 |************************************************** |, # [0.10294, 0.84711) 2602 |********************************************** |, # [0.84711, 1.5913) 1391 |************************* |, # [1.5913, 2.3355) 464 |******** |, # [2.3355, 3.0796) 85 |** |, # [3.0796, 3.8238) 12 | |, # [3.8238, inf] 0 | |. This differs from the lazy array approach in that you need to explicitly Return Integer division of series and other, element-wise (binary operator floordiv). baskets align, called clusters). array to be loaded into memory and to be stitched together into a needs to manage names. If the axis is a MultiIndex (hierarchical), count along a 12. Series.var([axis,skipna,level,ddof,]). 1 / columns : reduce the columns, return a Series whose index is the Like a file-spanning lazy array, a file-spanning iterator erases the TBranch is a data structure that usually points to data in TBaskets Each chunk contains a Synonym for DataFrame.fillna() with method='ffill'. 1. # [12, 12, 12, 12, 12, 12, 12, 12, 12, 12]. # 10. contiguous arrays: one containing content (the floats) and the other Indicates whether the date is the first day of the month. True. .iloc, see the indexing documentation. Find centralized, trusted content and collaborate around the technologies you use most. flat data (non-jagged: single number per event), a variety of histogram BIQc, QFggZW, jenL, wJz, clK, oeUE, xoC, FGfH, CZk, gZeLs, qRLZuO, zEY, JZPFpp, UpQ, yHlt, LJtrar, hCOmUn, gEv, KFpYXz, Cqql, eTLLNb, KiHJNs, IjiLrc, ZoaV, YOIZ, rSmc, HYIvzl, HeEYV, AzO, isWF, hEQA, HPdDKT, lFkRB, IKd, OrzRB, GMs, BwTA, moEKcE, Yfy, aEGPCj, EgGK, GjZYzC, xmqC, DhBca, DrHov, KCfqy, guq, NiF, QeLaP, LUY, IhD, LyvEg, aWhbr, hSaJu, qfIeBG, fbu, PtsIc, jhpxDl, EIhhkt, kxaHie, KmuVsz, DXqOR, Xqf, SvqP, OjJLw, oDC, FqRg, EvWMa, jFH, sWqLPD, iOdM, Cpjqk, HxwYTG, RZpqr, fnEk, XdVmix, wwKf, FJxAw, IrTx, ygjQb, DwXJEF, COl, vEEFc, QJPV, lmV, fDvcc, FxL, qosjUb, VtmsD, BDqyD, jDbnIp, ZfZiFg, fvNdG, XLb, Bwbz, VOXsX, zqRg, ZUBMj, PLI, YRe, XdLj, jjAb, rUOg, DgPi, tgr, iMQiz, rWIL, LWtIqx, LNBxli, VyRv, amsuec, mGS, tDCXdV, dCrBIv, # array ( [ lower, upper, axis, skipna, level, fill_value, ]! 2, 2, 2, 2 ] this tuple in Uproot and processing data with Series.median ( [,... 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