If there's a range of columns you must have, then you can use r = df.val.groupby([df.cat, df.val]).sum().unstack().fillna(0).astype(int) for c in set(range(1, 7)) - set(df.val.unique()): r[c] = 0

May 25, 2017 · Instead of doing it in one step, you can do the aggregation firstly and then pivot it using unstack method: .groupby(level='ptype') .apply(lambda g: g.apply(pd.value_counts)) .unstack(level=1) .fillna(0)) Another option to avoid using apply method: .groupby(level=[0,1]) .value_counts() .unstack(level=[1,2]) .fillna(0) .sort_index(axis=1))

Feb 12, 2024 · Creating Pivot Table with Multiple Columns using Pandas Pivot Table for Students Report. Let's create a DataFrame (df) with columns Name, Subject, Score, and Grade. Subsequently, a pivot table is generated using the pivot_table method, and the 'Name' column is designated as the index.

Mar 4, 2024 · This article focuses on manipulating data in Python with the Pandas library to create multi-dimensional pivot tables. Imagine having a dataset with sales information including dates, products, and regions. You need to analyze sales trends …

Nov 24, 2024 · We can create a Pandas pivot table with multiple columns and return reshaped DataFrame. By manipulating given index or column values we can reshape the data based on column values. Use the pandas.pivot_table to create …

Dec 23, 2022 · You can use the following basic syntax to create a frequency table in pandas based on multiple columns: df. value_counts ([' column1 ', ' column2 ']) The following example shows how to use this syntax in practice.

Nov 30, 2023 · Below are the ways by which we can iterate over columns of Dataframe in Python Pandas: Dataframe class provides a member function iteritems () which gives an iterator that can be utilized to iterate over all the columns of a data frame.

Jul 5, 2015 · How can I transform the following DataFrame into one with cities as rows and each cuisine as a column, and 1 or 0 as values (1 if the city has that kind of cuisine)? I think this turns out to be a very common problem in transforming data into features for machine learning.

Feb 24, 2025 · Creating a table in Python involves structuring data into rows and columns for clear representation. Tables can be displayed in various formats, including plain text, grids or structured layouts. Python provides multiple ways to generate tables, depending on the complexity and data size. Tabulate module is the most efficient way to create tables.

Since you want unique ones, you can apply unique () built-in pandas function. Then, since you will have a columns with a list of values, you can apply pd.Series to each row, to expand it into columns, by default, they are named from 0 to n, where n is the maximum length of a list, in this case, two. This would be a bit faster than using apply: