Pandas dataframe

　 　 DataFrame 是一个表格型的数据结构，它含有一组有序的列，每列可以是不同的值类型（数值、字符串、布尔型值）。DataFrame 既有行索引也有列索引，它可以被看做由 Series 组成的字典（共同用一个索引）。

dict

dict -> dataframe

d1 = {"columns":["Apple","Pear"],"data":[[12,0], [8,7], [1, 9]]}
df2 = pd.DataFrame(d1['data'])
   Apple  Pear
0     12     0
1      8     7
2      1     9

df2.columns=d1['columns']
Index(['Apple', 'Pear'], dtype='object')

dataframe -> dict

Syntax: DataFrame.to_dict(orient=’dict’, into=)

Parameters:

• orient: String value, (‘dict’, ‘list’, ‘series’, ‘split’, ‘records’, ‘index’) Defines which dtype to convert Columns(series into). For example, ‘list’ would return a dictionary of lists with Key=Column name and Value=List (Converted series).
• into: class, can pass an actual class or instance. For example in case of defaultdict instance of class can be passed. Default value of this parameter is dict.

Example

...
df2.to_dict('split')
{'index': [0, 1, 2], 'columns': ['Apple', 'Pear'], 'data': [[12, 0], [8, 7], [1, 9]]}

df2.to_dict('records')
[{'Apple': 12, 'Pear': 0}, {'Apple': 8, 'Pear': 7}, {'Apple': 1, 'Pear': 9}]

list

list -> dataframe

　　See also: dict -> dataframe

dataframe -> list

a1 = df1.values    # values方法将dataframe转为numpy.ndarray
l1 = a1.tolist()
l1[0]              # get frist value
usys.utime(l1[0][7].value/10**9) * P.S. 日期型的字段转换后格式：Timestamp('2017-04-13 13:48:32')，pandas._libs.tslibs.timestamps.Timestamp. 可以使用 usys.utime(l1[0][7].value/10**9) 转换。

CSV

read

# 默认: sep=',', quotechar='"'
df1 = pandas.read_csv('test.csv', sep=',', quotechar='"', header=1)
# names 列不足，以末尾对齐
df2 = pandas.read_csv('test.csv', sep=',', quotechar='"', header=None, names=['c1', 'c2'])