Skip_after – skip lines at the end of the fileĭataFrame vaex. Skip_lines – skip lines at the start of the file Names – If True, the first line is used for the column names, otherwise provide a list of strings with names Seperator – value seperator, by default whitespace, use “,” for comma seperated values.
from_ascii ( "table.csv", seperator = ",", names = ) Parameters _information(x)Įstimate the mutual information between and x and y on a grid with shape mi_shape and mi_limits, possibly on a grid defined by binby. (expression)Ĭalculate the minimum and maximum for expressions, possibly on a grid defined by binby. (expression)Ĭalculate the maximum for given expressions, possibly on a grid defined by binby. (expression)Ĭalculate the minimum for given expressions, possibly on a grid defined by binby. _approx(.)Ĭalculate the median, possibly on a grid defined by binby. (x)Ĭalculate the correlation coefficient cov/(std*std) between x and y, possibly on a grid defined by binby. (x)Ĭalculate the covariance matrix for x and y or more expressions, possibly on a grid defined by binby. (expression)Ĭalculate the sample variance for the given expression, possible on a grid defined by binby (expression)Ĭalculate the standard deviation for the given expression, possible on a grid defined by binby (expression)Ĭalculate the mean for expression, possibly on a grid defined by binby. ()Ĭount the number of non-NaN values (or all, if expression is None or "*"). Vaex.vconstant(value, length)Ĭreates a virtual column with constant values, which uses 0 memory. Read a CSV file as a DataFrame, and optionally convert to an hdf5 file.Ĭreate a vaex DataFrame from an Astropy Table.Ĭreates a virtual column which is the equivalent of numpy.arange, but uses 0 memory om_json(path_or_buffer)Ī method to read a JSON file using pandas, and convert to a DataFrame directly. om_ascii(path)Ĭreate an in memory DataFrame from an ascii file (whitespace seperated by default). om_pandas(df)Ĭreate an in memory DataFrame from a pandas DataFrame.
Open a DataFrame from file given by path.Ĭreate an in memory dataset from a dict with column names as keys and list/numpy-arrays as valuesĬreate an in memory DataFrame from numpy arrays, in contrast to from_arrays this keeps the order of columns intact (for Python < 3.6).Ĭreate an in memory DataFrame from numpy arrays.Ĭreates a vaex DataFrame from an arrow Table.Ĭreate a DataFrame from an Apache Arrow datasetĬreate a Vaex DataFrame from a Vaex Dataset API documentation for vaex library ¶ Quick lists ¶ Opening/reading in your data.