Stack Overflow
stackoverflow.com › questions › 74362719 › using-set-with-pandas
python - using set() with pandas - Stack Overflow
x = pd.DataFrame(df1, colmns=[0]) set(x.iloc[:,0].values)
GeeksforGeeks
geeksforgeeks.org › pandas › create-a-set-from-a-series-in-pandas
Create A Set From A Series In Pandas - GeeksforGeeks
July 23, 2025 - We can directly apply set() function to the pandas series, the set function automatically convert the pandas series into a set.
Videos
03:33
Pandas Set Index | pd.DataFrame.set_index() - YouTube
02:32
Set Index of pandas DataFrame in Python (Example) | How to Convert ...
10:00
How I Use the Pandas Set Index Function - YouTube
03:53
Pandas Set Values | pd.DataFrame.at[], .iat[] - YouTube
Pandas Conditional Columns: Set Pandas Conditional ...
16:58
Working with Large Data Sets Made Easy: Understanding Pandas Data ...
Pandas
pandas.pydata.org › docs › reference › api › pandas.DataFrame.set_index.html
pandas.DataFrame.set_index — pandas 3.0.1 documentation
Set the DataFrame index (row labels) using one or more existing columns or arrays (of the correct length).
Dontusethiscode
dontusethiscode.com › blog › 2024-03-06_indexes_and_sets.html
Python Set vs Pandas.Index
There we have it: key comparisons between the Index in pandas and Python's built-in, set.
KDnuggets
kdnuggets.com › 2019 › 11 › set-operations-applied-pandas-dataframes.html
Set Operations Applied to Pandas DataFrames - KDnuggets
November 7, 2019 - In this tutorial, we show how to apply mathematical set operations (union, intersection, and difference) to Pandas DataFrames with the goal of easing the task of comparing the rows of two datasets.
Pandas
pandas.pydata.org › docs › user_guide › options.html
Options and settings — pandas 3.0.1 documentation
The API is composed of 5 relevant functions, available directly from the pandas namespace: get_option() / set_option() - get/set the value of a single option.
Medium
medium.com › @rodwan.bakkar › pandas-set-difference-fde7f4381b53
Pandas Set Difference. We need in many situation to find the… | by Rodwan Bakkar | Medium
August 19, 2019 - Pandas Set Difference We need in many situations to find the difference of two data frames in pandas. In other words we need to find the rows which belong to one data frame and do not belong to the …
Pandas
pandas.pydata.org › docs › user_guide › dsintro.html
Intro to data structures — pandas 3.0.1 documentation
We’ll start with a quick, non-comprehensive overview of the fundamental data structures in pandas to get you started. The fundamental behavior about data types, indexing, axis labeling, and alignment apply across all of the objects.
Pandas
pandas.pydata.org › pandas-docs › stable › generated › pandas.DataFrame.html
pandas.DataFrame — pandas 3.0.1 documentation - PyData |
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W3Schools
w3schools.com › python › pandas › ref_df_set_index.asp
Pandas DataFrame set_index() Method
import pandas as pd data = { "name": ... newdf = df.set_index('name') Try it Yourself » · The set_index() method allows one or more column values become the row index....
Spark By {Examples}
sparkbyexamples.com › home › pandas › create a set from a series in pandas
Create a Set From a Series in Pandas - Spark By {Examples}
March 27, 2024 - Use the pd.Series constructor to create a Series in Pandas. Transform the Series into a set using the set() function to obtain a unique collection of elements.
Pandas
pandas.pydata.org › docs › reference › api › pandas.Series.cat.set_categories.html
pandas.Series.cat.set_categories — pandas 3.0.1 documentation
For pandas.Series: >>> raw_cat = pd.Categorical( ... ["a", "b", "c", None], categories=["a", "b", "c"], ordered=True ... ) >>> ser = pd.Series(raw_cat) >>> ser 0 a 1 b 2 c 3 NaN dtype: category Categories (3, str): ['a' < 'b' < 'c'] >>> ser.cat.set_categories(["A", "B", "C"], rename=True) 0 A 1 B 2 C 3 NaN dtype: category Categories (3, str): ['A' < 'B' < 'C'] For pandas.CategoricalIndex: >>> ci = pd.CategoricalIndex( ...
W3Schools
w3schools.com › python › pandas › pandas_dataframes.asp
Pandas DataFrames
A Pandas DataFrame is a 2 dimensional data structure, like a 2 dimensional array, or a table with rows and columns.
Pandas
pandas.pydata.org › docs › reference › general_functions.html
General functions — pandas 3.0.1 documentation
melt(frame[, id_vars, value_vars, var_name, ...]) · Unpivot a DataFrame from wide to long format, optionally leaving identifiers set
Pandas
pandas.pydata.org › docs › reference › api › pandas.set_option.html
pandas.set_option — pandas documentation
Set the value of the specified option or options.
Pandas
pandas.pydata.org › docs › reference › api › pandas.Series.at.html
pandas.Series.at — pandas 3.0.1 documentation
Similar to loc, in that both provide label-based lookups. Use at if you only need to get or set a single value in a DataFrame or Series.
Top answer 1 of 2
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If you only need to get list of unique values, you can just use unique method.
If you want to have Python's set, then do set(some_series)
In [1]: s = pd.Series([1, 2, 3, 1, 1, 4])
In [2]: s.unique()
Out[2]: array([1, 2, 3, 4])
In [3]: set(s)
Out[3]: {1, 2, 3, 4}
However, if you have DataFrame, just select series out of it ( some_data_frame['<col_name>'] ).
2 of 2
43
With large size series with duplicates the set(some_series) execution-time will evolve exponentially with series size.
Better practice would be to set(some_series.unique()).
Pandas
pandas.pydata.org › docs › reference › api › pandas.DataFrame.at.html
pandas.DataFrame.at — pandas 3.0.1 documentation
Similar to loc, in that both provide label-based lookups. Use at if you only need to get or set a single value in a DataFrame or Series.
Pandas
pandas.pydata.org › docs › reference › api › pandas.DataFrame.html
pandas.DataFrame — pandas 3.0.1 documentation
Data structure also contains labeled axes (rows and columns). Arithmetic operations align on both row and column labels. Can be thought of as a dict-like container for Series objects. The primary pandas data structure.