Ref:

Ref:

Ref:

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Ref:

• pandas.DataFrame()
• pandas.Series()
• pandas.read_csv()
• pandas.DataFrame.shape
• pandas.DataFrame.head
• pandas.read_excel()
• pandas.to_csv()
• pandas.to_excel()

Ref:

• pandas.iloc(): 类似于Excel中的Cell函数，将其看做Matrix
• pandas.loc()

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一、基本概念

class

pandas.

DataFrame

(data=None, index=None, columns=None, dtype=None, copy=False)

Parameters:

data : 数据主体部分，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 : 行名称，默认 0, 1, 2, ..., n, 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 : 列名称，默认 0, 1, 2, ..., n, 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 : 数据类型，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

 

data[1:,0] means the first column, data[0,1:] means the first row.

>>> import numpy as np>>> import pandas as pd>>> data = np.array([	['','Col1','Col2'],	['Row1',1,2],	['Row2',3,4]	])>>> print(pd.DataFrame(data=data[1:,1:],		       index=data[1:,0],		       columns=data[0,1:]))     Col1 Col2Row1    1    2Row2    3    4

or

>>> data = np.array([	[1,2],	[3,4]])>>> print(pd.DataFrame(data=data,		       index=['Row1','Row2'],		       columns=['Col1','Col2']))      Col1  Col2Row1     1     2Row2     3     4

Ref:

Ref:

Ref:

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二、相关方法：

DataFrame.apply(func, axis=0, broadcast=None, raw=False, reduce=None, result_type=None, args=(), **kwds)

Apply a funciton along an axis of the DataFrame. (类似Excel中对一列或者一行数据进行摸个函数的处理)

Objects passed to the function are Series objects whose index is either the DataFrame's index (axis=0) or the DataFrame's columns (axis=1).

Ref:

Series.value_counts(normalize=False, sort=True, ascending=False, bins=None, dropna=True)

Returns object containing counts of unique values.

The resulting object will be in desceding order so that the first element is the most frequent-occurring element. Excludes NA values by default.

:  可以将 Str 通过 转为文件缓存，可以直接用此方法

>>> from io import StringIO>>> a = '''A, B, C1,2,34,5,67,8,9'''>>> a'\nA, B, C\n1,2,3\n4,5,6\n7,8,9\n'>>> data = pd.read_csv(StringIO(a))>>> data   A   B   C0  1   2   31  4   5   62  7   8   9