The next sections explain these steps in more detail. In order to retrieve the data into the DataFrame, you must invoke a method that performs an action (for example, the Specify how the dataset in the DataFrame should be transformed.įor example, you can specify which columns should be selected, how the rows should be filtered, how the results should beĮxecute the statement to retrieve the data into the DataFrame. Sense, a DataFrame is like a query that needs to be evaluated in order to retrieve data.Ĭonstruct a DataFrame, specifying the source of the data for the dataset.įor example, you can create a DataFrame to hold data from a table, an external CSV file, from local data, or the execution of a SQL statement. AĭataFrame represents a relational dataset that is evaluated lazily: it only executes when a specific action is triggered. To retrieve and manipulate data, you use the DataFrame class. In Snowpark, the main way in which you query and process data is through a DataFrame. Python (requires third-party lxml package for stylesheet) import pandas as pdĮmployees_df = pd.read_xml("myInput.xml", stylesheet="myScript.Working with DataFrames in Snowpark Python ¶ Change as needed after parsing in Python. Sample data includes a complete XML capturing top earners in pandas and xml StackOverflow tags and using seafaring ranks.ĭo note: the brackets and parentheses are not included being disallowed symbols in XML node names. Ideally I want the value of the custom attribute to be added to the column header with normal brackets () and the datatype value in square brackets Ĭonsider XSLT, the special-purpose language designed to transform XML files, which is supported in pandas.read_xml() using the default lxml parser and stylesheet argument.īelow XSLT will flatten content to employee level, drawing down ancestor elements, and dynamically handling salary element depending on attributes. The attributes in question are 'Custom' where the value might be 'Yes' and 'datatype' where the values could be 'int', 'string' etc. I am just unable to target the attributes I need. The data below the headers isn't important to me, as from here I will pull only the headers into a new master excel document to create what will ultimately become a sort of report definition / data dictionary where I can parse the XML files of all the reports used across an organisation. So I need to get both and then add them to what is ultimately the columns in my dataframe / list. Now this works when there is only one, however I need to be able to achieve a similar result when there is multiple attributes, the example I am working on has two ("custom" and "datatype"). company department employee name job salary (Custom)
#DATAFRAME ADD A LIST AS ELEMENT CODE#
Using the below code I can see which fields have an attribute and then add the text 'custom' to the head. Import os #-lib to extract filename for input and folder movement Import openpyxl as xl #-lib to convert csv to xls Import pandas as pd #-lib to read csv file Import xlwings as xw #-lib to do most of the excel steps Imports are as follows: #-import libraries I then combine that with the column header to identify that column has an attribute (as not all will). The end goal is being able to flag, first if there is an attribute and then the value of that attribute. I'm currently trying to parse some XML (XML added at end of question) in Python to get the value of multiple attributes.
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