It also has group by functionality like Pandas and can be used with multiple expressions like filter. Polar data frame on car values greater than 2 The above code filters out rows where values are above 2 on column B. The Polar data frame allows conditions to be applied to slice the data using filters. When printing out a data frame it also outputs the size automatically displayed by shape in a tuple format. It has similar syntax to the Pandas library for creating a data frame using the dictionary syntax. It uses a standard composable pattern and allows runs in parallel. The Python Polars is a data frame library written in Rust and can be much faster than Pandas data frames when doing data processing on large datasets. Polars data frame library written using Rust programming lanuage Overall Taipy provides a really useful UI with easy coding and high completeness in comparison to Plotly another web app which is a little more complex in its coding. Taipy Core creates scenarios, uses models, retrieve metrics easily and applies version control to application configuration. The http link will open a page in your browser that will look like Webpage created using markdown syntax This deploys a local web server like so * Server starting on Gui(page="# Getting started with *Taipy*").run() ![]() It uses markdown notation and formats to build simple web pages, see example below from taipy import Gui Taipy GUI can create an interactive and powerful user interface with a few lines of code. It is composed of two main independent components Taipy Core and Taipy GUI. Taipy is a new low-code Python package that allows you to create data science applications for graphical visualization, managing algorithms, pipelines, and scenarios on web applications quickly. A low-code Python library to build full web applications It had a variety of diverse speakers and was really interesting to see the latest trends and tooling. It was an in-person event at the Leonardo Royal Hotel at London Tower Bridge. ![]() ('4-Grain Flakes, Riihikosken Vehnämylly', 'fibre'): 11.I recently attended the PyData Conference in London, it was a really interesting collection of talks, demos and workshops on Python packages. You can then generate a dictionary as before: d = final_df.loc), :].to_dict() You need to just use multi-index slicing: fibre_df = final_df.loc), :]Ĥ-Grain Flakes, Riihikosken Vehnämylly fibre 11.2 It is also possible to get your final example of a multidict, directly from the multi-indexed dataframe. ('4-Grain Flakes, Riihikosken Vehnämylly', 'fibre'): 11.2, ('4-Grain Flakes, Riihikosken Vehnämylly', 'energy'): 1443.0, ![]() Now we can simply use to to_dict() method of the datframe to create the dictionary you are looking for: nutritionValues = df1.to_dict() We can do this easily by extracting as an n * 3 NumPy array (using the values attribute of the dataframe) and then flattening the matrix, using NumPy's ravel method: df1 = pd.DataFrame(df.values.ravel(), index=multi_ix, columns=)Ĥ-Grain Flakes, Riihikosken Vehnämylly id 32570.0 To populate this dataframe, notice that we simple need to row-wise values from columns. Now we can create a new dataframe using out multi_ix. We can create the MultiIndex from this list of tuples as follows: multi_ix = pd.om_tuples(index_tuples) Others.remove("name") # We don't want "name" to be included We end with a list of tuples: names = df.name.tolist() I will use lists, within a list comprehension, where I bundle up the values together into tuples. Now we can create the combinations of each value in "name" with each of the other column names. This will then generate a dictionary of the form you want.įirst I just recreate your example dataframe (would be nice if you provide this code in the future!): import pandas as pdĭf = pd.DataFrame()ĭf.columns = + list(df.columns)ġ 4-Grain Flakes, Gluten Free 35146 1569 6.1Ģ 4-Grain Flakes, Riihikosken Vehnämylly 32570 1443 11.2 ![]() In order to be able to create a dictionary from your dataframe, such that the keys are tuples of combinations (according to your example output), my idea would be to use a Pandas MultiIndex.
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