plydata.helper_verbs.arrange_if¶
-
class
plydata.helper_verbs.
arrange_if
(*args, **kwargs)[source]¶ Arrange by all column that match a predicate
- Parameters
- data
dataframe
, optional Useful when not using the
>>
operator.- predicate
function
A predicate function to be applied to the columns of the dataframe. Good candidates for predicate functions are those that check the type of the column. Such function are avaible at
pandas.api.dtypes
, for examplepandas.api.types.is_numeric_dtype()
.For convenience, you can reference the
is_*_dtype
functions with shorter strings:'is_bool' # pandas.api.types.is_bool_dtype 'is_categorical' # pandas.api.types.is_categorical_dtype 'is_complex' # pandas.api.types.is_complex_dtype 'is_datetime64_any' # pandas.api.types.is_datetime64_any_dtype 'is_datetime64' # pandas.api.types.is_datetime64_dtype 'is_datetime64_ns' # pandas.api.types.is_datetime64_ns_dtype 'is_datetime64tz' # pandas.api.types.is_datetime64tz_dtype 'is_float' # pandas.api.types.is_float_dtype 'is_int64' # pandas.api.types.is_int64_dtype 'is_integer' # pandas.api.types.is_integer_dtype 'is_interval' # pandas.api.types.is_interval_dtype 'is_numeric' # pandas.api.types.is_numeric_dtype 'is_object' # pandas.api.types.is_object_dtype 'is_period' # pandas.api.types.is_period_dtype 'is_signed_integer' # pandas.api.types.is_signed_integer_dtype 'is_string' # pandas.api.types.is_string_dtype 'is_timedelta64' # pandas.api.types.is_timedelta64_dtype 'is_timedelta64_ns' # pandas.api.types.is_timedelta64_ns_dtype 'is_unsigned_integer' # pandas.api.types.is_unsigned_integer_dtype
No other string values are allowed.
- functions
callable()
ortuple
ordict
orstr
Functions to alter the columns before they are sorted:
function (any callable) - Function is applied to the column and the result columns replace the original columns.
tuple
of functions - Each function is applied to all of the columns and the name (__name__
) of the function is postfixed to resulting column names.dict
of the form{'name': function}
- Allows you to apply one or more functions and also control the postfix to the name.str
- String can be used for more complex statements, but the resulting names will be terrible.
Note that, the functions do not change the data, they only affect the sorting.
- args
tuple
Arguments to the functions. The arguments are pass to all functions.
- kwargs
dict
Keyword arguments to the functions. The keyword arguments are passed to all functions.
- data
Notes
Do not use functions that change the order of the values in the array. Such functions are most likely the wrong candidates, they corrupt the data. Use function(s) that return values that can be sorted.
Examples
>>> import pandas as pd >>> import numpy as np >>> from plydata import * >>> df = pd.DataFrame({ ... 'alpha': list('aaabbb'), ... 'beta': list('babruq'), ... 'theta': list('cdecde'), ... 'x': [1, 2, 3, 4, 5, 6], ... 'y': [6, 5, 4, 3, 2, 1], ... 'z': [7, 9, 11, 8, 10, 12] ... })
Arranging by the columns with strings in ascending order.
>>> df >> arrange_if('is_string') alpha beta theta x y z 1 a a d 2 5 9 0 a b c 1 6 7 2 a b e 3 4 11 5 b q e 6 1 12 3 b r c 4 3 8 4 b u d 5 2 10
Arranging by the columns with strings in descending order.
>>> df >> arrange_if('is_string', pd.Series.rank, ascending=False) alpha beta theta x y z 4 b u d 5 2 10 3 b r c 4 3 8 5 b q e 6 1 12 2 a b e 3 4 11 0 a b c 1 6 7 1 a a d 2 5 9
It is easier to sort by only the numeric columns in descending order.
>>> df >> arrange_if('is_numeric', np.negative) alpha beta theta x y z 5 b q e 6 1 12 4 b u d 5 2 10 3 b r c 4 3 8 2 a b e 3 4 11 1 a a d 2 5 9 0 a b c 1 6 7