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### Pandas #### ##### + ************ + **** + [0,1] + *X = (X-min)/(max-min)* + max + min + max-min + + **** + 0 1 + *X=(x - x)/* + **X** + **** + [0,1] + **** + [-1,1] + *X=X/10^k* + 1. [0,1] 2. 3. + ```python import numpy as np import pandas as pd infos = pd.read_excel("./.xlsx", index_col=0) print(":\n", infos) # -- # def min_max_scalar(val): # return (val- val.min()) / (val.max() - val.min()) # # # infos["score"] = infos["score"].transform(min_max_scalar) # infos["hight"] = infos["hight"].transform(min_max_scalar) # print(":\n", infos) # # def stand_scalar(val): # mean_val = val.mean() # std_val = val.std() # return (val-mean_val) /std_val # # # infos["score"] = infos["score"].transform(stand_scalar) # infos["hight"] = infos["hight"].transform(stand_scalar) # print(":\n", infos) # , def abs_max_log_scalar(val): # exp1 = val.abs() # exp2 = exp1.max() # exp3 = np.log10(exp2) # exp4 = np.ceil(exp3) # k = 10 ** exp4 k = 10 ** (np.ceil(np.log10((val.abs()).max()))) return val / k infos["score"] = infos["score"].transform(abs_max_log_scalar) infos["hight"] = infos["hight"].transform(abs_max_log_scalar) print(":\n", infos) ``` ##### + **** 1. **** : 2. **`pd.get_dummies(data, prefix=None, prefix_sep="_", dummy_na=False, columns=None, sparse=False, drop_first=False, dtype=None,)` : ** + | | | | -------------- | ------------------------------------------------------------ | | **data** | SeriesDataFrame | | **prefix** | Noneprefix="A", | | **prefix_sep** | sepration"_" | | **dummy_na** | boolFalseFalse NaNNaN | | **columns** | DataFramecolumnsdtype | | **sparse** | `SparseArray`TrueNumPyFalse | | **drop_first** | False | | **dtype** | dtype | + **** + + + + : ****** ** + **** + pandas cut + `pd.cut(x, bins, right: bool = True, labels=None, retbins: bool = False, precision: int = 3, include_lowest: bool = False, duplicates: str = "raise", ordered: bool = True,)` : + | | | | ------------------ | ------------------------------------------------------------ | | **x** | array-like1DataFrame | | **bins** | bins3intpandas.IntervalIndex int. binsintxbinsx0.1%x. binx .pandas.IntervalIndex
| | **right** | boolTruebins=[1,2,3]right=True(1,2](2,3]right=False(1,2),(2,3) | | **labels** | binsxbinslabelsbins=[1,2,3]2(1,2](2,3]labels2labels=Falsexbin0 | | **retbins** | boolbinsbinsintFalse | | **precision** | 3. | | **include_lowest** | boolfalse`duplicates``raise``drop` | + : + **** + + `cut()` `qcut()` + **** + 1. K-Means 2. 3. + k-Means k-Means + +

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