1.Series

Generate one-dimensional array, left index, right value:

In [3]: obj = Series([1,2,3,4,5])
In [4]: obj
Out[4]:
0    1
1    2
2    3
3    4
4    5
dtype: int64
In [5]: obj.values
Out[5]: array([1, 2, 3, 4, 5], dtype=int64)
In [6]: obj.index
Out[6]: RangeIndex(start=0, stop=5, step=1)

 

Create an index to mark each data point:

In [7]: obj2 = Series([4,1,9,7], index=["a","c","e","ff"])
In [8]: obj2
Out[8]:
a     4
c     1
e     9
ff    7
dtype: int64
In [9]: obj2.index
Out[9]: Index(['a', 'c', 'e', 'ff'], dtype='object')

 

Take a value or a set of values:

In [10]: obj2["c"]
Out[10]: 1
In [11]: obj2[["c","e"]]
Out[11]:
c    1
e    9
dtype: int64

 

Array operation, the index will be displayed:

In [12]: obj2[obj2>3]
Out[12]:
a     4
e     9
ff    7
dtype: int64

 

Series can also be regarded as an orderly dictionary. Many dictionary operations can be used:

In [13]: "c" in obj2
Out[13]: True

 

To create a Series directly from a dictionary:

In [14]: data = {"name":"liu","year":18,"sex":"man"}
In [15]: obj3 = Series(data)
In [16]: obj3
Out[16]:
name    liu
year     18
sex     man
dtype: object

 

To create a Series with a dictionary and a list:

In [17]: list1 = ["name","year","mobile"]
In [18]: obj4 = Series(data,index=list1)
In [19]: obj4
Out[19]:
name      liu
year       18
mobile    NaN
dtype: object

PS: because there is no mobile in the data dictionary, the value is NaN

 

Check whether the data is missing:

In [20]: pd.isnull(obj4)
Out[20]:
name      False
year      False
mobile     True
dtype: bool
 
In [21]: pd.notnull(obj4)
Out[21]:
name       True
year       True
mobile    False
dtype: bool
 
In [22]: obj4.isnull()
Out[22]:
name      False
year      False
mobile     True
dtype: bool
 
In [23]: obj4.notnull()
Out[23]:
name       True
year       True
mobile    False
dtype: bool

 

name attribute of Series:

In [7]: obj4.name = "hahaha"
In [8]: obj4.index.name = "state"
In [9]: obj4
Out[9]:
state
name      liu
year       18
mobile    NaN
Name: hahaha, dtype: object

 

2.DataFrame

Building DataFrame

In [13]: data = {
"state":[1,1,2,1,1],
"year":[2000,2001,2002,2004,2005],
"pop":[1.5,1.7,3.6,2.4,2.9]
}
In [14]: frame = DataFrame(data)
In [15]: frame
Out[15]:
   state  year  pop
0      1  2000  1.5
1      1  2001  1.7
2      2  2002  3.6
3      1  2004  2.4
4      1  2005  2.9

 

Set the names of the rows and columns. If the data cannot be found, NA value will be generated:

In [18]: frame2 = DataFrame(
data,
columns=["year","state","pop","debt"],
index=["one","two","three","four","five"]
)
In [19]: frame2
Out[19]:
       year  state  pop debt
one    2000      1  1.5  NaN
two    2001      1  1.7  NaN
three  2002      2  3.6  NaN
four   2004      1  2.4  NaN
five   2005      1  2.9  NaN

 

Get the columns of DataFrame as Series:

In [7]: frame2.year
Out[7]:
one      2000
two      2001
three    2002
four     2004
five     2005
Name: year, dtype: int64

PS: the returned index does not change, and the name property is set

 

Get row:

In [11]: frame2.loc["three"]
Out[11]:
year     2002
state       2
pop       3.6
debt      NaN
Name: three, dtype: object

 

Assignment column:

In [12]: frame2['debt'] = 16.5
In [13]: frame2
Out[13]:
       year  state  pop  debt
one    2000      1  1.5  16.5
two    2001      1  1.7  16.5
three  2002      2  3.6  16.5
four   2004      1  2.4  16.5
five   2005      1  2.9  16.5

 

If you assign a list or an array, the length needs to be equal; if you assign a Series, the index is exactly matched

In [17]: val = Series([1.2,1.5,1.7], index=["two","four","five"])
In [18]: frame2['debt'] = val
In [19]: frame2
Out[19]:
       year  state  pop  debt
one    2000      1  1.5   NaN
two    2001      1  1.7   1.2
three  2002      2  3.6   NaN
four   2004      1  2.4   1.5
five   2005      1  2.9   1.7

 

If the column does not exist, create:

In [21]: frame2["eastern"] = frame2.state == 1
In [22]: frame2
Out[22]:
       year  state  pop  debt  eastern
one    2000      1  1.5   NaN     True
two    2001      1  1.7   1.2     True
three  2002      2  3.6   NaN    False
four   2004      1  2.4   1.5     True
five   2005      1  2.9   1.7     True

 

For nested dictionaries, DataFrame will be interpreted as a column on the outer layer and a row index on the inner layer:

In [23]: dic = {"name":{"one":"liu","two":"rui"},"year":{"one":"23","two":"22"}}
In [24]: frame3 = DataFrame(dic)
In [25]: frame3
Out[25]:
    name year
one  liu   23
two  rui   22

 

Display row, column name:

In [26]: frame3.index.name = "index"
In [27]: frame3.columns.name = "state"
In [28]: frame3
Out[28]:
state name year
index
one    liu   23
two    rui   22

 

Return data in the form of 2D ndarray:

In [29]: frame3.values
Out[29]:
array([['liu', '23'],
       ['rui', '22']], dtype=object)

 

3. Index object

In [30]: obj = Series(range(3),index=["a","b","c"])
In [31]: index = obj.index
In [32]: index
Out[32]: Index(['a', 'b', 'c'], dtype='object')

 

The index object is immutable, so that index can be shared in multiple data structures

In [35]: index = pd.Index(np.arange(3))
In [36]: obj2 = Series([1.5,0.5,2],index=index)
In [37]: obj2.index is index
Out[37]: True