pandas的DataFrame用法
用來生成DataFrame數據
1.說明:
class?pandas.DataFrame(data=None,?index=None,?columns=None,?dtype=None,?copy=False)
Two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). Arithmetic operations align on both row and column labels. Can be thought of as a dict-like container for Series objects. The primary pandas data structure.
| data?: numpy ndarray (structured or homogeneous), dict, or DataFrame Dict can contain Series, arrays, constants, or list-like objects Changed in version 0.23.0:?If data is a dict, argument order is maintained for Python 3.6 and later. index?: Index or array-like Index to use for resulting frame. Will default to RangeIndex if no indexing information part of input data and no index provided columns?: Index or array-like Column labels to use for resulting frame. Will default to RangeIndex (0, 1, 2, …, n) if no column labels are provided dtype?: dtype, default None Data type to force. Only a single dtype is allowed. If None, infer copy?: boolean, default False Copy data from inputs. Only affects DataFrame / 2d ndarray input |
?
代碼:
1 import tensorflow 2 import lightgbm as lgb 3 import pandas as pd 4 import numpy as np 5 6 class Deng(object): 7 def __init__(self): 8 pass 9 10 def main(self): 11 temp = ['a', 'a', 'b', 'c', 'c'] 12 st = pd.Categorical(temp) 13 print(st) 14 # [a, a, b, c, c] 15 # Categories(3, object): [a, b, c] 16 17 # 遍歷temp指出temp中每個字符所屬類別的位置索引 18 st2 = st.codes 19 print(st2) 20 # [0 0 1 2 2] 21 22 def gen_data(self): 23 df = pd.DataFrame(data=np.eye(3), columns=['c1', 'c2', 'c3']) 24 print(df) 25 26 27 if __name__ == '__main__': 28 obj = Deng() 29 obj.gen_data()輸出:
c1 c2 c3 0 1.0 0.0 0.0 1 0.0 1.0 0.0 2 0.0 0.0 1.0?
轉載于:https://www.cnblogs.com/demo-deng/p/9614489.html
總結
以上是生活随笔為你收集整理的pandas的DataFrame用法的全部內容,希望文章能夠幫你解決所遇到的問題。
- 上一篇: 命名实体识别遇到的问题
- 下一篇: Faster-RCNN