1. About pandas
The two major data structures in pandas are Series and Dataframe.
Series is similar to an object with an indexed one-dimensional array. Unlike the value, it has an additional label, so data can be obtained according to the label. At the same time, Series can be thought of as an ordered dictionary.
Dataframe is a tabular data structure that contains an ordered column. The data structure of different columns can be different, and the data type of the same column can be the same.
2. Some common operations of Series
import numpy as np
import pandas as pd
import sys
from pandas import Series, DataFrame
obj = Series([4, 7, -5, 3])
obj
Out[129]:
0 4
1 7
2 -5
3 3
dtype: int64
In [130]:
obj.values
Out[130]:
array([ 4, 7, -5, 3], dtype=int64)
In [131]:
obj.index#Get the index value
Out[131]:
RangeIndex(start=0, stop=4, step=1)
In [132]:
obj2 = Series([4, 7, -5, 3], index=['d', 'b', 'a', 'c'])
obj2
Out[132]:
d 4
b 7
a -5
c 3
dtype: int64
In [133]:
obj2.index
Out[133]:
Index(['d', 'b', 'a', 'c'], dtype='object')
In [134]:
obj2['a']#Get the corresponding value according to the index
Out[134]:
-5
In [135]:
obj2['d'] = 6
obj2[['c', 'a', 'd']]
Out[135]:
c 3
a -5
d 6
dtype: int64
In [136]:
obj2[obj2 > 0]
Out[136]:
d 6
b 7
c 3
dtype: int64
In [137]:
obj2 * 2
Out[137]:
d 12
b 14
a -10
c 6
dtype: int64
In [138]:
np.exp(obj2)
Out[138]:
d 403.428793
b 1096.633158
a 0.006738
c 20.085537
dtype: float64
In [139]:
#Index is not in the series index value
'b' in obj2#The index is not in the series index value
Out[139]:
True
In [140]:
#Create series from dictionary
sdata = {'Ohio': 35000, 'Texas': 71000, 'Oregon': 16000, 'Utah': 5000}
obj3 = Series(sdata)
obj3
Out[140]:
Ohio 35000
Oregon 16000
Texas 71000
Utah 5000
dtype: int64
In [141]:
states = ['California', 'Ohio', 'Oregon', 'Texas']
obj4 = Series(sdata, index=states)
obj4
Out[141]:
California NaN
Ohio 35000.0
Oregon 16000.0
Texas 71000.0
dtype: float64
In [142]:
pd.isnull(obj4)#Detect true value
Out[142]:
California True
Ohio False
Oregon False
Texas False
dtype: bool
In [143]:
pd.notnull(obj4)
Out[143]:
California False
Ohio True
Oregon True
Texas True
dtype: bool
In [144]:
obj3
Out[144]:
Ohio 35000
Oregon 16000
Texas 71000
Utah 5000
dtype: int64
In [145]:
obj4
Out[145]:
California NaN
Ohio 35000.0
Oregon 16000.0
Texas 71000.0
dtype: float64
In [146]:
obj3 + obj4
Out[146]:
California NaN
Ohio 70000.0
Oregon 32000.0
Texas 142000.0
Utah NaN
dtype: float64
In [147]:
obj4.name = 'population'
obj4.index.name = 'state'
obj4
Out[147]:
state
California NaN
Ohio 35000.0
Oregon 16000.0
Texas 71000.0
Name: population, dtype: float64
In [148]:
#Modify the index value through assignment
obj.index = ['Bob', 'Steve', 'Jeff', 'Ryan']
obj
Out[148]:
Bob 4
Steve 7
Jeff -5
Ryan 3
dtype: int64
In [ ]:
3. Common operations of Dataframe
