Pandas series DataFrame row and column data filtering, pandasdataframe
I. Cognition of DataFrame DataFrame is essentially a row (index) column index + multiple columns of data.
To simplify our understanding, let's change our thin
lines for GD and HN, you can do this:
In [8]: Df[df.p1.isin ([' GD ', ' HN '])]out[8]: p1 p2 p30 GD GX FJ2 HN HB AH
But if we want data beyond these two lines, we need to get around the point.
The principle is to first remove the P1 and convert it to a list, then remove the unwanted rows (values) from the list and then use them in the Dataframeisin()
In [9]: Ex_list = List (DF.P1) in [ten]: Ex_list.remove (' GD ') in [all]: Ex_list.remove (' HN ') in []: ex_listout[12]: [' SD ', ' HE N ', ' sh
Using Python for data analysis (7)-pandas (Series and DataFrame), pandasdataframe 1. What is pandas? Pandas is a Python data analysis package based on NumPy for data analysis. It provides a large number of advanced data structures and data processing methods. Pandas has two
Let's create a data frame by hand.[Python]View PlainCopy
Import NumPy as NP
Import Pandas as PD
DF = PD. DataFrame (Np.arange (0,2). Reshape (3), columns=list (' abc ' )
DF is such a dropSo how do you choose the three ways to pick the data?One, when each column already has column name, with DF [' a '] can choose to take out a whole column of data. If you know column names and
already has column name, use data [' col1 '] to choose to take out an entire column of data. If you know column names and index, you can choose. loc simultaneously row and column selection: Data.loc[index, ' colum_names '] iloc functionUse the method with the LOC function, but no longer enter the column name, but the index:data.iloc[row_index,col_index of the input column]The functions of the IX function I
Ming 6.0 - Name:price, Dtype:float64 -Zhang San 1.2 theReese 1.0 -Harry 2.3 -Chen Jiu 5.0 -Xiao Ming 6.0 +Name:price, Dtype:float64 In general, we often need to value by column, then Dataframe provides loc and Iloc for everyone to choose from, but the difference is between the two.1 Print(frame2)2 Print(frame2.loc['Harry'])#Loc can use the index of the string type, whereas the Iloc can only be of type int
This section describes the basic methods of data in series and Dataframe
Re-index
An important method of Pandas objects is reindex, which is to create a new object that adapts to the new index" "Created on 2016-8-10@author:xuzhengzhu" "" "Created on 2016-8-10@author:xuzhengzhu" " fromPandasImport*Print
Import NumPy as NP from
Pandas import dataframe
import pandas as PD
Df=dataframe (Np.arange () reshape (3,4 ), index=[' One ', ' two ', ' THR '],columns=list (' ABCD ')
df[' A ' #取a列
df[[' A ', ' B ']] #取a, column B
#ix可以用数字索引, You can also use
I believe many people like me in the process of learning Python,pandas data selection and modification has a great deal of confusion (perhaps by the Matlab) impact ...
To this day finally completely figure out ...
Let's start with a data box manually.
Import NumPy as NP
import pandas as PD
DF = PD. Dataframe (Np.arange (0,60,2). Reshape (10,3), columns=list (' a
A Data box is a two-dimensional data structure, similar to a table in SQL. Data boxes can be constructed using dictionaries, arrays, lists, and sequences.
1. If the dictionary data box is created, the column name is the key name:
d = {‘one‘:pd.Series([1,2,3],index= [‘a‘,‘b‘,‘c‘]), ‘two‘:pd.Series([1,2,3,4],index=[‘a‘,‘b‘,‘c‘,‘d‘])}print(pd.DataFrame(d))
2. List creation data box:
d = pd.DataFrame([[1,2,
This question mainly writes the method of sorting series and dataframe according to index or value
Code:
#coding =utf-8
Import pandas as PD
import numpy as NP
#以下实现排序功能.
SERIES=PD. Series ([3,4,1,6],index=[' B ', ' A ', ' d ', ' C '])
FRAME=PD. Dataframe ([[2,4,1,5],[3,1,4,
1.0 1.01.4, Join_axesDF1 = PD. DataFrame (Np.ones ((3,4)) *0, columns = [' A ', ' B ', ' C ', ' d '], Index=[1, 2, 3])DF2 = PD. DataFrame (Np.ones (3,4) * *, columns = [' B ', ' C ', ' d ', ' e '], index=[2, 3, 4]) result=pd.concat ([DF1,DF2], Axis=1)A b c D b c D E1 0.0 0.0 0.0 0.0 nan nan nan nan2 0.0 0.0 0.0 0.0 1.
1 concat
The Concat function is a method underneath the pandas that allows for a simple fusion of data based on different axes.
Pd.concat (Objs, axis=0, join= ' outer ', Join_axes=none, Ignore_index=false, Keys=none, Levels=none, Names=None,
Verify_integrity=false)1 2 1 2 1 2
Parameter descriptionObjs:series,dataframe or a sequence of panel compositions lsitAxis: Axis that needs to
Python array, list, And dataframe index slicing operations: July 22, July 19, 2016-zhi Lang document,Array, list, And dataframe index slicing operations: January 1, July 19, 2016-zhi Lang document
List, one-dimensional, two-dimensional array, datafrme, loc, iloc, and ix
Numpy array
We choose the DataFrame from these three levels: rows, regions, cells.The corresponding method of use is as follows:A. Row, column--df[]Two. Area--df.loc[], df.iloc[], df.ix[]Three. Cell--df.at[], df.iat[]Here's how to start the exercise:Import NumPy as NP Import = PD. DataFrame (Np.random.randn (6,4), index=list ('abcdef'), columns=list ('ABCD '))1. DF[]:One-d
The following for you to share a pandas implementation of the selection of a specific index of the row, has a good reference value, I hope to be helpful to everyone. Come and see it together.
As shown below:
>>> Import numpy as np>>> import pandas as pd>>> Index=np.array ([2,4,6,8,10]) >>> Data=np.array ([3,5,7,9,
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