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If data is a Series, then data['a'] returns all values with the index value of a. However, if data is a DataFrame, then data['a'] returns all values in the column(s) named a. To avoid this ambiguity, Pandas supports the syntax data.loc['a'] as an alternative way to filter using the index. Pandas also supports the syntax data.iloc[n], which ...
Many statistical and data processing systems have functions to convert between these two presentations, for instance the R programming language has several packages such as the tidyr package. The pandas package in Python implements this operation as "melt" function which converts a wide table to a narrow one. The process of converting a narrow ...
A pivot table is a table of values which are aggregations of groups of individual values from a more extensive table (such as from a database, spreadsheet, or business intelligence program) within one or more discrete categories. The aggregations or summaries of the groups of the individual terms might include sums, averages, counts, or other ...
easily adding a new column if many elements of the new column are left blank (if the column is inserted and the existing fields are unnamed, use a named parameter for the new field to avoid adding blank parameter values to many template calls)
The Marshall-Edgeworth index, credited to Marshall (1887) and Edgeworth (1925), [11] is a weighted relative of current period to base period sets of prices. This index uses the arithmetic average of the current and based period quantities for weighting. It is considered a pseudo-superlative formula and is symmetric. [12]
If ties are present in the data set, the simplified formula above yields incorrect results: Only if in both variables all ranks are distinct, then [] [] = [ [] ] = [ [] ] = (calculated according to biased variance). The first equation — normalizing by the standard deviation — may be used even when ranks are ...
To get round this, we note that the coefficient will have its largest value where the smallest ranks are all opposite the 0s and the largest ranks are opposite the 1s. Its smallest value occurs where the reverse is the case. These values are respectively plus and minus (n 1 + n 0)/2. We can therefore use the reciprocal of this value to rescale ...
The data below the 5th percentile lie between −40 and −5 inclusive, while the data above the 95th percentile lie between 101 and 1053 inclusive (pertinent values are shown in bold). Winsorization effectively resets the outlier values to the values of the data at the 5th and 95th percentiles.