Supports xlsxlsxxlsmxlsband odf file extensions read from a local filesystem or URL. Supports an option to read a single sheet or a list of sheets. Any valid string path is acceptable.
Python – Pandas – Converting XLSX to Dictionary (key and values)
The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is expected. If you want to pass in a path object, pandas accepts any os. By file-like object, we refer to objects with a read method, such as a file handler e. Strings are used for sheet names. Integers are used in zero-indexed sheet positions. Specify None to get all sheets. Defaults to 0 : 1st sheet as a DataFrame. Row 0-indexed to use for the column labels of the parsed DataFrame.
If a list of integers is passed those row positions will be combined into a MultiIndex. Use None if there is no header. List of column names to use. Column 0-indexed to use as the row labels of the DataFrame.
The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Is it possible to output this matrix into excel or to a csv file so that it has the following format? I have looked into using csv. DictWriter but can not produce the desired output. Probably not the most minimal result, but pandas would solve this marvellously and if you're doing data analysis of any kind, I can highly recommend pandas!
Learn more. Asked 4 years, 4 months ago. Active 2 years, 6 months ago. Viewed 9k times. I have the following dictionary in python that represents a From - To Distance Matrix. Jeremy Jeremy 2 2 gold badges 5 5 silver badges 13 13 bronze badges. Active Oldest Votes. DataFrame graph. T transpose to look just like the sheet above df. Works perfectly Thank you! Let me take this one step further.Asus framework service reddit
Would it be possible, given a set of dataframe pairs, to sum their corresponding values from the dataframe? Sure, with a given list of pairs, i. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name.
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Dark Mode Beta - help us root out low-contrast and un-converted bits.However, there are instances when I just have a few lines of data or some calculations that I want to include in my analysis. In these cases it is helpful to know how to create DataFrames from standard python lists or dictionaries.
The basic process is not difficult but because there are several different options it is helpful to understand how each works. Normally, through some trial and error, I figure it out. Since it is still confusing to me, I thought I would walk through several examples below to clarify the different approaches. In this case each dictionary key is used for the column headings. Using this approach, you get the same results as above.
The key point to consider is which method is easier to understand in your unique situation. Sometimes it is easier to get your data in a row oriented approach and others in a column oriented. Most of you will notice that the order of the columns looks wrong. The issue is that the standard python dictionary does not preserve the order of its keys.
For reasons I outline below, I tend to specifically re-order my columns vs.Mcr3u factoring worksheets
In order to keep the various options clear in my mind, I put together this simple graphic to show the dictionary vs.
If this is a little hard to read, you can also get the PDF version. This may seem like a lot of explaining for a simple concept. The secret sauce here is to use startrow to write the footer DataFrame below the sales DataFrame. There is also a corresponding startcol so you can control the column layout as well. However, there are times when you will have data in a basic list or dictionary and want to populate a DataFrame.
I tend to like the list based methods because I normally care about the ordering and the lists make sure I preserve the order. On the surface, these samples may seem simplistic but I do find that it is pretty common that I use these methods to generate quick snippets of information that can augment or clarify the more complex analysis. We are a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for us to earn fees by linking to Amazon.
Toggle navigation. Practical Business Python Taking care of business, one python script at a time. DataFrame sales. Created by Created on Version 0 CM 1. ExcelWriter 'simple-report. Updates Nov As of pandas 0. You can use DataFrame. If you want to preserve order, you can use DataFrame.Example Python program that converts a pandas DataFrame into a Python dictionary.
Create a DataFrame. Convert the DataFrame to Series. Contents of the DataFrame:. DataFrame as a dictionary:. Example Python program that converts a pandas DataFrame into a. Python dictionary in list mode. Create DataFrame. Convert the DataFrame to dictionary. Daily temperature from DataFrame:.
DataFrame as a dictionary List orientation :. Toggle navigation Pythontic. The output can be specified of various orientations using the parameter orient. In dictionary orientation, for each column of the DataFrame the column value is listed against the row label in a dictionary.
All these dictionaries are wrapped in another dictionarywhich is indexed using column labels. Dictionary orientation is the default orientation for the conversion output. In list orientation, each column is made a list and the lists are added to a dictionary against the column labels. In series orientation, each column is made a pandas Seriesand the series instances are indexed against the row labels in the returned dictionary object.
In split orientation, each row is made a list and they are wrapped in another list and indexed with the key "data" in the returned dictionary object. The row labels are stored in a list against the key "index". The columns labels are stored in a list against the key "columns".
In records orientation, each column is made a dictionary where the column elements are stored against the column name.
All the dictionaries are returned as a list.A simple function to convert the dataframe to dictionary. If you see the Name key it has a dictionary of values where each value has row index as Key i. The first index of dataframe is 0 which is the first key of dictionary and has a dictionary of a row as value and each value inside this dictionary has column header as Key. So we are setting the index of dataframe as Name first and then Transpose the Dataframe and convert it into a dictionary with values as list.
It returns list of dictionary and each row values is a dictionary having colum label as key and timestamp object as their values. You can specify the type from the collections. Mapping subclass used for all Mappings in the return value. For example: the into values can be dict, collections.
OrderedDict and collections.
You can also group the values in a column and create the dictionary. Now we are interested to build a dictionary out of this dataframe where the key will be Name and the two Semesters Sem 1 and Sem 2 will be nested dictionary keys and for each Semester we want to display the Grade for each Subject.
As you can see in the following code we are using a Dictionary comprehension along with groupby to achieve this. We have set the index to Name and Sem which are the Keys of each dictionary and then grouping this data by Name.
And iterating this groupy object inside the dictionary comprehension to get the desired dictionary format.Ip address for sprint hotspot
So just to summarize our key learning in this post, here are some of the main points that we touched upon:. Your email address will not be published. OrderedDict [ 'A', OrderedDict [ 0, Timestamp ' '1, Timestamp ' ' ]'B', OrderedDict [ 0, Timestamp ' '1, Timestamp ' ' ] ].Sentinel locomotive drawings
OrderedDict [ 'A'. OrderedDict [ 0Timestamp ' '. Facebook 0 Tweet 0 Pin 0 LinkedIn 0. Leave a Reply Cancel reply Your email address will not be published.During data import process in a Jupyter Notebookusing Python and Pandas module for data science, we would need to manipulate or import directly an Excel file into a notebook and transfor all the data into a dictionary, so in this article we will focus on this particular need.
A library is just a set of reusable functions. The pandas library is available for free online. We tell our current Jupyter notebook that we are about to use it with the line import pandas. You can see that when we access the first element of this list, it returns our first dictionary. Now that we have the data in Pythonwe can use a couple of other of functions to quickly explore our data.
If we just look at the first element we see both the keys and values. If we would like to also see our values of a dictionary in a list, we can do something similar with the. Once again, we call the method, and then coerce it into a list, by using the list function. As you can see, by using the keyword dict, we can pass through the name of the key followed by the equal sign.
Notice that the key name is provided as a string, but Python still knows how to handle it. You can use these like in the following code:.
Below there is a function approach, using xlrd library, to import and transform Excel data into a dictionary :.
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Data Science Stack Exchange is a question and answer site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field. It only takes a minute to sign up. I want to convert this into a dictionary with name as a key and list of dictionaries value1 key and value2 value for all values that are in name. So, far I have managed to get a dictionary with name as key and list of only one of the values as a list by doing.
How do I get my desired output? Is there a way to aggregate all the values for the same name column and get them in the form I want? Setting the 'ID' column as the index and then transposing the DataFrame is one way to achieve this.
Building on nemo's answer above which will be faster than the accepted solution, this will give the same output that you want:. Sign up to join this community. The best answers are voted up and rise to the top.
Home Questions Tags Users Unanswered. Export pandas to dictionary by combining multiple row values Ask Question. Asked 1 year, 10 months ago.
Active 4 days ago. Viewed 30k times. The data needs to be in a format like that for some further processing which is sadly on in my control. Active Oldest Votes. Thanks a lot. Aditya Aditya 1, 1 1 gold badge 10 10 silver badges 31 31 bronze badges. My requirement is a little different.
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How to Export Pandas DataFrame to an Excel File
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