pandas的DataFrame用法

用来生成DataFrame数据

1.说明：

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.

Parameters:	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

Parameters:

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

代码：

 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         # 遍历temp指出temp中每个字符所属类别的位置索引
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()

输出：

    c1   c2   c3
0  1.0  0.0  0.0
1  0.0  1.0  0.0
2  0.0  0.0  1.0

转载于:https://www.cnblogs.com/demo-deng/p/9614489.html

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