Python Learning 23: One of the pandas (Series)

pandas is constructed based on the module NumPy containing make faster and easier data analysis, and data structures of the operating tool, the most common data structures are: the sequence data block and DataFrame Series, Series similar to the one-dimensional array of numpy, similar to the relationship a table; and DataFrame similar to the two-dimensional table.

>>> import pandas as pd
>>> from pandas import Series,DataFrame

A, pandas data type

With dtype property to display data element type, pandas dtype mainly the following:

object: string type indicates
int: an integer of type
float: floating point representation type
datetime: indicates the time type
bool: Boolean representation
category: Category

1, view data types

View data box, type a column of data:

df['col_name'].dtypes

2, data type conversion

astype (dtype) function for converting the data frame to a specific type of column, pandas DTYPE may be a supported type may be numpy.dtype, Python type may be:

Column to change the data frame to a string, str, python type, 'object' is pandas string types supported:

df['col_name'].astype(str)
df['col_name'].astype('object')

3, other types of conversion functions

Pandas using functions provided as to_numeric (), to_datetime ()

Second, the sequence

Series is an ordered set of data and associated index composition, the elements can be accessed by an index Series object.

1, create a sequence

Using only one set of data can be generated simplest Series, at this time the index is sequentially incremented from 0 integer:

obj=Series([4,7,-5,3])

Index sequence and the value can be viewed by the index attribute values and object Series:

obj.values
## array([4,7,-5,3])
obj.index
## Int64Index([0,1,2,3])

You can use the list to create a custom sequence:

obj=Series([4,7,-5,3],index=['d','b','a','c'])

If the data is stored in a Python dictionary structure can also be created directly by Series dictionary:

>>> sdata = {'b': 12, 'a': 13, 'd': 14, 'c': 10}
>>> sd=pd.Series(sdata)
b    12
a    13
d    14
c    10
dtype: int64

2, the sequence element access

To access the elements of the sequence by an index, and can be modified value sequence elements

sd [ ' a ' ] = 4

Reference documents: