Python: Pandas Data Manipulations

Data Manipulations

Python Data Manipulation functions are: melt(), pivot(), pivot_table(), crosstab(), cut(), qcut(), merge(), merge_ordered(), merge_asof(), concat(), get_dummies(), factorize(), unique() and wide_to_long()

Functions Name Description
melt() Unpivot a DataFrame from wide format to long format, optionally leaving identifier variables set.
pivot(data[, index, columns, values]) Return reshaped DataFrame organized by given index / column values.
pivot_table(data[, values, index, columns, ...]) Create a spreadsheet-style pivot table as a DataFrame.
crosstab(index, columns[, values, rownames, ...]) Compute a simple cross tabulation of two (or more) factors.
cut(x, bins[, right, labels, retbins, ...]) Bin values into discrete intervals
qcut(x, q[, labels, retbins, precision, ...]) Quantile-based discretization function.
merge(left, right[, how, on, left_on, ...]) Merge DataFrame or named Series objects with a database-style join.
merge_ordered(left, right[, on, left_on, ...]) Perform merge with optional filling/interpolation designed for ordered data like time series data.
merge_asof(left, right[, on, left_on, ...]) Perform an asof merge.
concat(objs[, axis, join, join_axes, ...]) Concatenate pandas objects along a particular axis with optional set logic along the other axes.
get_dummies(data[, prefix, prefix_sep, ...]) Convert categorical variable into dummy/indicator variables.
factorize(values[, sort, order, ...]) Encode the object as an enumerated type or categorical variable.
unique(values) Hash table-based unique.
wide_to_long(df, stubnames, i, j[, sep, suffix]) Wide panel to long format.

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