python pandas slice columns

Python pandas common functions, pythonpandas This article focuses on pandas common functions.1 import Statement import pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport datetimeimport re2. File Reading Df = pd.read_csv(path+'file.csv ')Parameter: header = None use the default column name, 0, 1, 2, 3

Use the pandas framework of Python to perform data tutorials in Excel files, Introduction The purpose of this article is to show you how to use pandas to execute some common Excel tasks. Some examples are trivial, but I think it is equally important to present these simple things with complex functions that you can find elsewhere. As an extra benefit, I will perf

the row of headers and tails in frame, default 5 rows# Print Df.head ()# Print Df.tail (3)# 2. Display indexes, columns, and underlying numpy data# Print Df.index# Print Df.columns# Print Df.values# # 3.describe () function for statistical summary of data, methods in Python cannot omit parentheses# Print Df.describe ()# 4. Transpose the data# Print DF. T# 5. Sort by axis (column)# Print Df.sort_index (axis

In the field of data analysis, the most popular is the Python and the R language, before an article "Don't talk about Hadoop, your data is not big enough" point out: Only in the size of more than 5TB of data, Hadoop is a reasonable technology choice. This time to get nearly billions of log data, tens data is already a relational database query analysis bottleneck, before using Hadoop to classify a large number of text, this time decided to use

This article mainly introduces the use of Python in the Pandas Library for CDN Log analysis of the relevant data, the article shared the pandas of the CDN log analysis of the complete sample code, and then detailed about the pandas library related content, the need for friends can reference, the following to see togeth

PandasPandas is the most powerful data analysis and exploration tool under Python. It contains advanced data structures and ingenious tools that make it fast and easy to work with data in Python. Pandas is built on top of NumPy, making numpy-centric applications easy to use. Pandas is very powerful and supports SQL-lik

chunk size to read and then call the Pandas.concat connection dataframe,chunksize set at about 10 million speed optimization is more obvious. loop = Truechunksize = 100000chunks = []while loop: try: chunk = Reader.get_chunk (chunkSize) chunks.append ( Chunk) except stopiteration: loop = False print "Iteration is stopped." DF = Pd.concat (chunks, ignore_index=true) Here is the statistics, read time is the data read times, total time is read and

This article describes how to use the pandas library in Python to analyze cdn logs. It also describes the complete sample code of pandas for cdn log analysis, then we will introduce in detail the relevant content of the pandas library. if you need it, you can refer to it for reference. let's take a look at it. This art

." Using different block sizes to read and then call Pandas.concat connection Dataframe,chunksize set at about 10 million speed optimization is more obvious. loop = True chunksize = 100000 chunks = [] while loop: try: chunk = Reader.get_chunk (chunksize) chunks.append (chunk) except stopiteration: loop = False print "Iteration is stopped." DF = Pd.concat (chunks, ignore_index=true) The following is the statistical data, read time is the data read times, total

Python programming: getting started with pandas and getting started with pythonpandas After finding the time to learn pandas, I learned a part of it first, and I will continue to add it later. Import pandas as pdimport numpy as npimport matplotlib. pyplot as plt # create a sequence for

-02 foo train 3 1 2013-01-02 foo Test 3 1 2013-01-02 foo train 3 1 2013-01-02 15. Sort by value>>> Df2.sort (columns='B', ascending=True) A B C D2013-01-02-0.665522-2.935955 1.249425 0.9023902013-01-01-0.941915-1.304691-0.837790-0.8051012013-01-04 1.362527- 1.059686-1.564129-1.2675062013-01-06-0.863264-0.548317 0.277112 1.2338252013-01-05 0.719452-0.152727 0.319914-0. 4485352013-01-03-0.419268 0.750735-0.547377-0.075151>>> Df2.sort (

automatically added as index Here you can simply replace index, generate a new series, People think, for NumPy, not explicitly specify index, but also can be through the shape of the index to the data, where the index is essentially the same as the numpy of the Shaping indexSo for the numpy operation, the same applies to pandas At the same time, it said that series is actually a dictionary, so you can also use a

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

Preface Recent work encountered a demand, is to filter some data according to the CDN log, such as traffic, status code statistics, TOP IP, URL, UA, Referer and so on. Used to be the bash shell implementation, but the log volume is large, the number of logs of G, the number of rows up to billies level, through the shell processing a little bit, processing time is too long. The use of the data Processing library for the next Python

Pandas is the most famous data statistics package in Python environment, and Dataframe is a data frame, which is a kind of data organization, this article mainly introduces the pandas in Python. Dataframe the row and column summation and add new row and column sample code, the text gives the detailed sample code, the n

NgLineParser import pandas as pdimport socketimport struct class PDNgLogStat (object ): def _ init _ (self): self. ng_line_parser = NgLineParser () def _ log_line_iter (self, pathes): "" parse each row in the file and generate an iterator "for path in pathes: with open (path, 'R') as f: for index, line in enumerate (f): self. ng_line_parser.parse (line) yield self. ng_line_parser.to_dict () def _ ip2num (self, ip): "used to convert an IP address to a

: Conda's package management is better understood, this part of the function is similar to PIP. 2. Setting the editor environment and templates My editor uses the Pycharm ability to set up development environments and templates for rapid development. Anaconda settings: Fixed template settings: #-*-Coding:utf-8-*-"" "@author: Corwien@file:${name}.py@time:${date}${time}" "" 3. PIP Command Installation NumPy Installation MacOS # Use Python:p ip3 install numpy# using python:p IP install numpy Lin

write in front: by yesterday's record we know, pandas.read_csv (" file name ") method to read the file, the variable type returned is dataframe structure . Also pandas one of the most core types in . That in pandas there is no other type Ah, of course there are, we put dataframe type is understood to be data consisting of rows and columns, then dataframe

', ' C ', ' d ', ' e '])Two discards the item on the specified axisThe data on a row can be discarded by means of a drop , and the parameter is the row indexin [+]: objOUT[64]:1 42 73 54 3Dtype:int64In [All]: New=obj.drop (1)in [+]: NewOUT[66]:2 73 54 3Dtype:int64Three-index, select and filterIn the list and tuple of Python, we can get the information we want by slicing, and we can also get the information by slicing in