2. Mapping
2.1 Mapping Meaningful statements: create a mapping relationship list, the elements and the values of a particular label or string binding
2.2 functions related operations:
2.2.1 the replace () function: Replace elements (DataFrame \ Series of Functions)
A. Function: df.replace (to_replace = None, value = None, InPlace = False, limit = None, REGEX = False, Method = 'PAD')
B. parameter analysis:
to_replace: STR, REGEX, List, dict, Series, int, a float or None, to be replaced value;
value: scalar, dictionaries, lists, str, regular expressions to replace value;
InPlace: True-place modification;
limit: int, limit the number of forward or backward filled;
regex : bool or to_replace same type; regular expressions;
Method: { 'PAD', 'ffill', 'backfill', 'bfill', None}; PAD / ffill: before using a non-missing values to fill in the missing values ; backfill / bfill: a non-missing value with filling the missing values
; None: specify a value to replace the missing value (dEFAULT this embodiment)
C1 to_replace = None, value = None combat:
Replace under specific field value:
replacing the value of a specified field:
Replace the value specified elements:
dictionary: Do not write value, with a dictionary of values replacement:
Regularization: replacement canonical form:
c2method combat
Baidu found this article when the replace function pandas replace function uses the summary , written in their case very meticulous, Amway to you, that is not operating diagram, can these two articles read in conjunction.
D. Small Note: DataFrame, you can not use the method and limit parameters
DataFrame replace operation:
- Alternatively a single value
- Replace ordinary
- Alternatively a single specified value column {column labels: replacement value}
- Multi-value replacement
- Replace list
- Replace single dictionary (recommended)
Series replace operation
to replace a single value
ordinary replacement
dictionary to replace
the multi-value replacement
list of alternatives
dictionaries replacement (recommended)
E. practice, I point oh
2.2.2 map () function: the new one (Series function)
A.map Description: x.map (Arg, na_action = None)
---- Map () can be mapped using the dictionary a new data
---- Map ( ) can be used in the expression lambd
---- Map () method can be used, may be a custom method
---- Map () function sum can not be used like, for loop
---- Map (Dictionary ) dictionary key data sufficient to match all, or occurs NaN3
B. parameter analysis:
Arg: function, dict, or Series
na_action: None {, 'the ignore'}
C. combat:
a C1 official small example:.
C2.map parameter is a function of:
C3.map parameter is expression
C4.map parameter is a dictionary:
2.2.3.transform () and map () is similar to:
A. use the function: x.transform (func, axis = 0 , * args, ** kwargs)
B. analytical parameters:
FUNC: function, str, list or dictionary
axis: {0, or 'index', 1, or 'columns'}, 0 by default; if 0 or 'index': applying a function to each column. If 1 or "column": applying a function to each row
* args: position parameter passed to func
** kwargs: Keyword parameters to be passed to func
C combat:
D. practice, I point oh
2.2.4 rename () function: the replacement index (DataFrame function)
A. using the function: DataFrame.rename (self, mapper = None , index = None, column = None, axis = None, copy = True, inplace = False, = None Level, errors = 'the ignore')
B. parameter analysis: reference official website
C. combat:
official example
: E. Summary
function replaces the row index using the rename ()
- mapper replace all indexes
- Replace the line index index
- Replace columns column index
- multidimensional index level specified dimensions
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