【338】Pandas.DataFrame

Ref: Pandas Tutorial: DataFrames in Python

Ref: pandas.DataFrame

Ref: Pandas：DataFrame对象的基础操作

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Ref: Creating, reading, and writing reference

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

Ref: Indexing, selecting, assigning reference

• 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.

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>>> 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 Col2 Row1    1    2 Row2    3    4

or

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

Ref: pandas dataframe.apply() 实现对某一行/列进行处理获得一个新行/新列

Ref: 在pandas中遍历DataFrame行

Ref: pandas.DataFrame.apply

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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: pandas.Series.value_counts

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.

DataFrame.read_csv():  可以将 Str 通过 StringIO() 转为文件缓存，可以直接用此方法

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>>> from io import StringIO >>> a = ''' A, B, C 1,2,3 4,5,6 7,8,9 ''' >>> a '\nA, B, C\n1,2,3\n4,5,6\n7,8,9\n' >>> data = pd.read_csv(StringIO(a)) >>> data    A   B   C 0  1   2   3 1  4   5   6 2  7   8   9