introduces you about Python in pandas. Dataframe to exclude specific lines of the method, the text gives a detailed example code, I believe that everyone's understanding and learning has a certain reference value, the need for friends to see together below.
2. About pandas in Python. Dataframe add a new row and column
background
Items
Pandas
Spark
Working style
Stand-alone, unable to process large amounts of data
Distributed, capable of processing large amounts of data
Storage mode
Stand-alone cache
Can call Persist/cache distributed cache
is variable
Is
Whether
Index indexes
Automatically created
No index
Row structure
Pandas.series
Pyspar
in the sense this they ' re an immutable data structure. Therefore things like:
# to create a new column "three"
df[' three ') = Df[' One '] * df[' one ']
Can ' t exist, just because this kind of affectation goes against the principles of Spark. Another example would is trying to access by index a single element within a DataFrame. Don ' t forget that your ' re using a distributed data structure, not a i
), columns=['A', 'B', 'C', 'D', 'E'])
DataFrame data preview:
A B C D E0 0.673092 0.230338 -0.171681 0.312303 -0.1848131 -0.504482 -0.344286 -0.050845 -0.811277 -0.2981812 0.542788 0.207708 0.651379 -0.656214 0.5075953 -0.249410 0.131549 -2.198480 -0.437407 1.628228
Calculate the total data of each column and add it to the end as a new column
df['Col_sum'] = df.apply(lambda x: x.sum(), axis=1)
Calculates the total data of each row and adds it to
[' col_sum ' = df.apply (lambda x:x.sum (), Axis=1)
Calculates the sum of each row's data and adds it to the end as a new row
df.loc[' row_sum ' = df.apply (lambda x:x.sum ())
Final data results:
A B C D E col_sum0 0.673092 0.230338-0.171681 0.312303-0.184813 0.8592381-0.504482-0.344286- 0.050845-0.811277-0.298181-2.0090712 0.542788 0.207708 0.651379-0.656214 0.507595 1.2532563-0.249410 0.131549-2.1984 80-0.437407 1.628228-1.125520row_sum 0.461987 0.225310-1.769627-1.592595 1.652828-1.0220
Pandas
Spark
Working style
Single machine tool, no parallel mechanism parallelismdoes not support Hadoop and handles large volumes of data with bottlenecks
Distributed parallel computing framework, built-in parallel mechanism parallelism, all data and operations are automatically distributed on each cluster node. Process distributed data in a way that handles in-memory data.Supports Hadoop and can handle large amounts of data
Pandas
Spark
Working style
Single machine tool, no parallel mechanism parallelismdoes not support Hadoop and handles large volumes of data with bottlenecks
Distributed parallel computing framework, built-in parallel mechanism parallelism, all data and operations are automatically distributed on each cluster node. Process distributed data in a way that handles in-memory data.Supports Hadoop and can handle large amounts of data
from:76713387How to iterate through rows in a DataFrame in pandas-dataframe by row iterationHttps://stackoverflow.com/questions/16476924/how-to-iterate-over-rows-in-a-dataframe-in-pandasHttp://stackoverflow.com/questions/7837722/what-is-the-most-efficient-way-to-loop-through-dataframes-with-pandasWhen it comes to manip
']], columns=['p1', 'p2 ...: ', 'p3'])In [4]: dfOut[4]: p1 p2 p30 GD GX FJ1 SD SX BJ2 HN HB AH3 HEN HEN HLJ4 SH TJ CQ
If you only want two rows whose p1 is 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
However, if we want data except the two rows, we need to bypass the point.
The principle is to first extract p1 and convert it to a list, then remove unnecessary rows (values) from the list, and then useisin()
In [9]: ex_list = list(df.p1)In [
Data type to force. Only a single dtype is allowed. If None, infer
Copy : boolean, default False
Copy data from inputs. Only affects dataframe/2d Ndarray input
See Also
DataFrame.from_records
constructor from tuples, also record arrays
DataFrame.from_dict
From Dic
This article mainly introduced the Python pandas in the Dataframe type data operation function method, has certain reference value, now shares to everybody, has the need friend to refer to
The Python data analysis tool pandas Dataframe and series as the primary data structures.
This article is mainly about how to oper
Forgive me for not having finished writing this article is a record of my own learning process, perfect pandas learning knowledge, the lack of existing online information and the use of Python data analysis This book part of the knowledge of the outdated,I had to write this article with a record of the situation. Most if the follow-up work is determined to have time to complete the study of Pandas Library,
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
[Python logging] importing Pandas Dataframe into Sqlite3 and dataframesqlite3
Use pandas. io connector to input Sqlite
Import sqlite3 as litefrom pandas. io import sqlimport pandas as pd
According to if_exists, input sqlite in three modes:
The following parameters are av
This time to bring you pandas+dataframe to achieve the choice of row and slice operation, pandas+dataframe to achieve the row and column selection and the attention of the slicing operation, the following is the actual case, take a look.
Select in SQL is selected according to the name of the column,
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 colum
How do I delete the list hollow character?Easiest way: New_list = [x for x in Li if x! = ']This section mainly learns the basic operations of pandas based on the previous two data structures.设有DataFrame结果的数据a如下所示: a b cone 4 1 1two 6 2 0three 6 1 6
First, view the data (the method of viewing the object is also applicable for series)1. View Dat
This article mainly introduces pandas in python. the DataFrame method for excluding specific rows provides detailed sample code. I believe it has some reference value for everyone's understanding and learning. let's take a look at it. This article mainly introduces pandas in python. the DataFrame method for excluding s
Previously written pandas DataFrame Applymap () functionand pandas Array (pandas Series)-(5) Apply method Custom functionThe applymap () function of the pandas DataFrame and the apply () method of the
The content source of this page is from Internet, which doesn't represent Alibaba Cloud's opinion;
products and services mentioned on that page don't have any relationship with Alibaba Cloud. If the
content of the page makes you feel confusing, please write us an email, we will handle the problem
within 5 days after receiving your email.
If you find any instances of plagiarism from the community, please send an email to:
info-contact@alibabacloud.com
and provide relevant evidence. A staff member will contact you within 5 working days.