pandas to csv

Sometimes you need to do some work on the values in the Pandas series , but without the built-in functions, you can write a function yourself, using the Pandas series 's apply method, You can call this function on each value inside, and then return a new SeriesImport= PD. Series ([1, 2, 3, 4, 5])def add_one (x): return x + 1print s.apply ( Add_one)# results:0 6dtype:int64A chestnut:Names =PD. Serie

Data conversionDelete duplicate elements　　The duplicated () function of the Dataframe object can be used to detect duplicate rows and return a series object with the Boolean type. Each element pairsshould be a row, if the row repeats with other rows (that is, the row is not the first occurrence), the element is true, and if it is not repeated with the preceding, the metaThe vegetarian is false.A Series object that returns an element as a Boolean is of great use and is particularly useful for fil

rate names = [' Bob ', ' Jessica ', ' Mary ', ' John ', ' Mel '] births = [968, 155, 77, 578, 973] Use the zip function to merge the two lists together. # Check the zip function's help zip? Babydataset = List (zip (names, births)) Babydataset [(' Bob ', 968), (' Jessica ', "), (' Mary ',), (' John ', 578), (' Mel ', 973)] We have completed the creation of a basic dataset. We now use Pandas to export this data to a

number, as the number of rows, directly with the index + assignment of the way to add.To find the maximum value of a column:Max_calories = food_info["energ_kcal"].max ()First locate the column that requires the maximum value, and then call the Max method directly to find the maximum value for a column.4, pandas the sort operationFood_info.sort_values ("Sodium_ (mg)", inplace=true)Print food_info["Sodium_ (mg)"]Call the Sort_values method on the DATAF

This article describes how the pandas series with the index index is vectorized:1. Index indexed arrays are the same:S1 = PD. Series ([1, 2, 3, 4], index=['a','b','C','D']) S2= PD. Series ([ten, +, +], index=['a','b','C','D'])PrintS1 +s2a11b22C33D44Dtype:int64Add the values corresponding to each index directly2. Index indexed array values are the same, in different order:S1 = PD. Series ([1, 2, 3, 4], index=['a','b','C','D']) S2= PD. Series ([ten, +,

The pandas Series is much more powerful than the numpy array , in many waysFirst, the pandas Series has some methods, such as:The describe method can give some analysis data of Series :Import= PD. Series ([1,2,3,4]) d = s.describe ()Print (d)Count 4.000000mean 2.500000std 1.290994min 1.00000025% 1.75000050% 2.50000075% 3.250000max 4.000000dtype:float64Second, the bigges

In [57]: df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar', ....: 'foo', 'bar', 'foo', 'foo'], ....: 'B' : ['one', 'one', 'two', 'three', ....: 'two', 'two', 'one', 'three'], ....: 'C' : np.random.randn(8), ....: 'D' : np.random.randn(8)}) ....: In [58]: dfOut[58]: A B C D0 foo one -1.202872 -0.0552241 bar one -1.814470 2.3959852 foo two 1.018601 1.5528253 bar three -0.595447 0.1665994 foo two 1.395433 0.0476095 bar two -0.392670 -0.1364736 foo one 0.007207 -0.5617577 foo thre

Import Pandasimport Pandas as PDCountries = ['Albania','Algeria','Andorra','Angola','Antigua and Barbuda', 'Argentina','Armenia','Australia','Austria','Azerbaijan', 'Bahamas','Bahrain','Bangladesh','Barbados','Belarus', 'Belgium','Belize','Benin','Bhutan','Bolivia']life_expectancy_values= [74.7, 75., 83.4, 57.6, 74.6, 75.4, 72.3, 81.5, 80.2, 70.3, 72.1, 76.4, 68.1, 75.2, 69.8, 79.4, 70.8, 62

Convert to CSV files-read CSV files, csv -- Convert to a CSV file: Http://www.dotnetgallery.com/lab/resource93-Export-to-CSV-file-from-Data-Table-in-Aspnet.aspx Http://www.devasp.net/net/articles/display/1330.html Http://stackoverflow.com/questions/16582993/how-to-save-a-dat

"Original" 10 minutes to fix pandasThis article is a simple translation of "Ten Minutes to Pandas" on the official website of Pandas, the original is here. This article is a simple introduction to pandas, detailed introduction please refer to:Cookbook . As a rule, we will introduce the required packages in the following format:First, create the objectYou can view

It's been a lot of red boxes all afternoon. Python2 and Python3 version conflicts Pip version IssuePip-v Updatesudo apt-get update sudo apt-get install Python-dev Finally do not know how to install, feeling is one of the following two ways‘‘‘ C++ sudo easy_install -U setuptools ‘‘‘ ‘‘‘ C++ sudo pip install --upgrade setuptools ‘‘‘ (Just beginning to try also not, do not know why suddenly magic can.) If not again, run both sides, see there is an answer is to run on both

The following for everyone to share a Python solution pandas processing missing value is an empty string problem, has a good reference value, I hope to help you. Come and see it together. Pit Record: Use pandas to do CSV missing value processing time found strange bug, that is, Excel open CSV file, obviously there i

']df_obj[' user number '].isin (alist) #将要过滤的数据放入字典中, uses Isin to filter the data, returns the row index and the results of each row filter, and returns if the match is turedf_obj[df_obj[' user number '].isin (alist)] #获取匹配结果为ture的行Filter data using Dataframe blur (like in sql):df_obj[df_obj[' package '].str.contains (R '. * Voice cdma.* ')] #使用正则表达式进行模糊匹配, * match 0 or unlimited, match 0 or 1 timesData conversion using Dataframe (post-supplemental instructions)df_obj[' branches _ Maintenance

was only 85.9 seconds. The next step is to process the null values in the remaining rows and, after testing, use an empty string in Dataframe.replace () to save some space than the default null value Nan, but for the entire CSV file, the empty column just saves one more "," so the 98 million x removed The 6 column also saved only 200M of space. Further data cleansing is still on the removal of unwanted data and merging. The drop of the data column,

seconds.The next step is to process the empty values in the remaining rows, and after testing, using an empty string in dataframe.replace () saves some space than the default null value Nan, but for the entire CSV file, the empty column only has one ",", so the removed 98 million The X 6 column also saves 200M of space. Further data cleansing is still the removal of useless data and merging.Discard the data column, in addition to invalid values and r

in the remaining rows, and after testing, using an empty string in Dataframe.replace () saves some space than the default null value Nan, but for the entire CSV file, the empty column is just one more ",", so the 98 million x removed The 6 column also saved only 200M of space. Further data cleansing is still the removal of useless data and merging. Discard the data column, in addition to invalid values and requirements, some of the table's own redund

Http://www.cnblogs.com/batteryhp/p/5006274.htmlPandas is the preferred library for subsequent content in this book. The pandas can meet the following requirements: Data structure with automatic or explicit data alignment by axis. This prevents many common errors caused by data misalignment and data from different data sources (indexed differently). Integrated time series capabilities Data structures that can handle time series data as

Recent work and Hive SQL to deal with more, occasionally encountered some problems of SQL is not easy to solve, will be downloaded to the file with pandas to deal with, due to the large amount of data, so there are some relevant experience can be shared with you, hope to learn pandas help YOU.Read and write large text dataSometimes we get a lot of text files, full read into the memory, read the process will

conversions CSV Data Set Read Structured data file reads HDF5 Read JSON data Set Read Excel reads Hive Table Read External database Read Index indexes Automatically created There are no index indexes and you need to create additional columns if needed Row structure Series structure, belonging to the pandas

Most of the students who Do data analysis start with excel, and Excel is the most highly rated tool in the Microsoft Office Series.But when the amount of data is very large, Excel is powerless, python Third-party package pandas greatly extend the functionality of excel, the entry takes a little time, but really is the necessary artifact of big data!1. Read data from a filePandas supports the reading of multiple format data, of course the most common a