Pandas学习，日积月累

关于ix，iloc，loc：

loc函数：完全基于标签位置的索引器，所谓标签位置就是下面定义的'row1','col2'等。primarily label based

iloc函数：完全基于index或者说integer的索引器。primarily integer position

通过以上两个函数并进行组合可以完成所有操作，ix为扩展，会经常报warning。

ix函数：supports mixed integer and label based access. It is primarily label based, but will fall back to integer positional access unless the corresponding axis is of integer type.

tips: 经过loc和iloc函数后的数据类型为series类型，与list类型相似，have the function of slice。不建议ix，warning影响美观

关于使用的实例：

import pandas as pd
data = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
rows = ['row1', 'row2', 'row3']  # 行标签
columns = ['col1', 'col2', 'col3']  # 列标签
df = pd.DataFrame(data, index=rows, columns=columns)
print(df)  # df: DataFrame type, use functions to access
print(df.loc['row1'])
print(df.loc['row1', 'col1'])  # based on label
print(df.loc['row1'][1])  # series type with slice function

print(df.iloc[0])
a = df.iloc[:, 1]  # a: series type have the function of [slice up]
print(df.iloc[:, 1])  # based on integer index
print(df.iloc[1]['col1'])  # combine with two methods

# not suggest, would have warning
print(df.ix[0][1])

# b type series  used double [[ && ]]
b = df.iloc[1][['col1','col2']]
# change types
print(list(b))

      col1  col2  col3
row1     1     2     3
row2     4     5     6
row3     7     8     9
col1    1
col2    2
col3    3
Name: row1, dtype: int64
1
col1    1
col2    2
col3    3
Name: row1, dtype: int64
row1    2
row2    5
row3    8
Name: col2, dtype: int64
4
2
[4, 5]

打不动字了，详细教程官方文档：https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#indexing-and-selecting-data

Pandas学习，日积月累

关于ix，iloc，loc：

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