Using Python for data analysis _pandas_ Foundation _2

Reindex method of basic ability re-indexing series

in [+]: obj = Series ([3,2,5,7,6,9,0,1,4,8],index=['a','b','C','D','e','F','g',    ...: 'h','I','J']) in [+]: obj1 = Obj.reindex (['a','b','C','D','e','F','g','h','I','J','k']) in [17]: obj1out[17]:a3.0b2.0C5.0D7.0e6.0F9.0g0.0h1.0I4.0J8.0k Nandtype:float64

The current value of the new index value is missing, you need to interpolate the value

The forward value fills method= ' Ffill ', and the last index J corresponds to the value to fill

in [+]: obj1 = Obj.reindex (['a','b','C','D','e','F','g','h','I','J','k'],metho ...: D='Ffill') in [20]: obj1out[20]:a3b2C5D7e6F9g 0h1I4J8k8Dtype:int64

Forward value handling method= ' pad ', last index J corresponds to the value to fill

In [all]: Obj1 = Obj.reindex (['a','b','C','D','e','F','g','h','I','J','k'],metho ...: D='Pad') in [24]: obj1out[24]:a3b2C5D7e6F9g 0h1I4J8k8Dtype:int64

The back value is populated with method= ' Bfill ', the index corresponding to the last index J is populated, and the next position of J is a blank line of Nan.

in [+]: Obj2 = Obj.reindex ([' A ', ' B ', ' C ', ' d ', ' e ', ' f ', ' G ', ' k ', ' h ', ' I ', ' J '],metho
...: d= ' Bfill ')

in [+]: obj2
OUT[63]:
A 3.0
B 2.0
C 5.0
D 7.0
E 6.0
F 9.0
G 0.0
K NaN
H 1.0
I 4.0
J 8.0
Dtype:float64

The trailing value is carried method= ' backfill ', the index corresponding to the last index J is populated, and the next position of J is a blank line of Nan.

in [+]: Obj2 = Obj.reindex (['a','b','C','D','e','F','g','k','h','I','J'],metho ...: D='Backfill') in [65]: obj2out[65]:a3.0b2.0C5.0D7.0e6.0F9.0g0.0k Nanh1.0I4.0J8.0Dtype:float64

The Reindex method of Dataframe

Modify (Row) an index, a column, or two changes.

When a sequence is introduced, the row is re-indexed, as follows:

in [+]: data = {'class':['language','Mathematics','English'],'score': [120,130,140]}in [[]: frame =DataFrame (data) in [88]: frameout[88]:  classScore0 Language1201 Mathematics 1302 English 140In [: frame2 = Frame.reindex ([0,1,2,3]) in [90]: frame2out[90]:  classScore0 Language120.01 Mathematics 130.02 English 140.03 nan nan

Rows and columns are modified

In [94]: Frame3 = frame.reindex (index=[11,22,33],columns = ['a','b') ,'C','d']) in [frame3out[]:  [A]:     a   b   c   D-a nan-nan nan nan-nan-nan-nan nan-nan-nan-nan NaN

The parameters of the Reindex are as follows:

Deletes the item series on the specified axis (index)

in []: obj = Series ([1,2,3,4],index=['a','b','C','D']) in [113]: objout[113]:a1b2C3D4dtype:int64in [[Obj1]: = Obj.drop ('C') in [115]: obj1out[115]:a1b2D4Dtype:int64

DataFrame

Delete a single index row

In [109]: frameout[109]:  class  score0    Chinese     1201    Math    1302    English    in[+]: obj = frame.drop (0) in [111]: objout[111]:   class  score1    math    1302    English    140

To delete a multi-index row

In [119]: frameout[119]:  class  score0    Chinese     1201    Math    1302    English    in[]: Frame.drop ([up]) out[  ]:class   score0    language     120

Delete multiple index rows (with axis)

in [[]: frameout[]  :class  score0    Chinese     1201    Mathematics    1302    English    in[131]: Frame.drop ([1,2],axis=0) out[131]:    Class  score0    language     120

Delete Column (columns) (with axis)

In [135]: frameout[135]:  class  score0    Chinese     1201    Math    1302    English    in[136]: Frame.drop (['class'],axis=1) out[ 136]:   score0    1201    1302    140

Where, axis=0, represents a row, Axis=1, represents a column

Using Python for data analysis _pandas_ Foundation _2