You can use all with boolean indexing:
print ((df == 'something1').all(1))
0 True
1 False
2 True
3 False
4 False
dtype: bool
print (df[(df == 'something1').all(1)])
col1 col2
0 something1 something1
2 something1 something1
EDIT:
If need select only some columns you can use isin with boolean indexing for selecting desired columns and then use subset - df[cols]:
print (df)
col1 col2 col3
0 something1 something1 a
1 something2 something3 s
2 something1 something1 r
3 something2 something3 a
4 something1 something2 a
cols = df.columns[df.columns.isin(['col1','col2'])]
print (cols)
Index(['col1', 'col2'], dtype='object')
print (df[(df[cols] == 'something1').all(1)])
col1 col2 col3
0 something1 something1 a
2 something1 something1 r
Top answer 1 of 3
16
You can use all with boolean indexing:
print ((df == 'something1').all(1))
0 True
1 False
2 True
3 False
4 False
dtype: bool
print (df[(df == 'something1').all(1)])
col1 col2
0 something1 something1
2 something1 something1
EDIT:
If need select only some columns you can use isin with boolean indexing for selecting desired columns and then use subset - df[cols]:
print (df)
col1 col2 col3
0 something1 something1 a
1 something2 something3 s
2 something1 something1 r
3 something2 something3 a
4 something1 something2 a
cols = df.columns[df.columns.isin(['col1','col2'])]
print (cols)
Index(['col1', 'col2'], dtype='object')
print (df[(df[cols] == 'something1').all(1)])
col1 col2 col3
0 something1 something1 a
2 something1 something1 r
2 of 3
15
Why not:
df[(df.col1 == 'something1') | (df.col2 == 'something1')]
outputs:
col1 col2
0 something1 something1
2 something1 something1
4 something1 something2
Pandas
pandas.pydata.org › docs › getting_started › intro_tutorials › 03_subset_data.html
How do I select a subset of a DataFrame? — pandas 3.0.6 documentation
Fare Cabin Embarked 1 2 1 1 ... 71.2833 C85 C 6 7 0 1 ... 51.8625 E46 S 11 12 1 1 ... 26.5500 C103 S 13 14 0 3 ... 31.2750 NaN S 15 16 1 2 ... 16.0000 NaN S [5 rows x 12 columns] To select rows based on a conditional expression, use a condition inside the selection brackets [].
Selecting Columns Based on Condition
If you don't want to do numeric_only because other columns also contain numbers, you can filter columns by name. As all the relevant columns have % in you can do df['total'] = df.filter(regex="%").sum(axis=1) Or you can pass in a list of column names to filter instead if you can't regex it. cols = ["Col 1 %", "Col 2 %", "Col 3 %"] df['total'] = df[cols].sum(axis=1) More on reddit.com
4
1
August 7, 2023Pandas Python, select columns based on rows conditions - Stack Overflow
Find the answer to your question by asking. Ask question ... See similar questions with these tags. ... Making the OWASP top ten in the vibe code... ... Help Shape the 2026 Developer Survey! 4250 How can I iterate over rows in a Pandas DataFrame? 3634 How do I select rows from a DataFrame based on column ... More on stackoverflow.com
Filtering a pandas float column by “less than”
Can also do df = df.query(“column_name < 100.0”) IMO this is never a bad option since it’s extremely concise and clear. Anyone familiar with SQL, excel, etc will immediately understand what they’re looking at. More on reddit.com
5
1
March 26, 2020How do I count the number of values greater than > 3 for every column in a Pandas DataFrame?
make some fake data: import pandas as pd d1 = {'S1' : [10,20,30,40,50], 'S2' : [100,200,300,400,500]} df = pd.DataFrame(d1) a filter for column 1 print(df[df.S1>20]) S1 S2 2 30 300 3 40 400 4 50 500 a filter for column 2 print(df[df.S2<200]) S1 S2 0 10 100 You can combine with & and | print(df[(df.S1>10) & (df.S2<300)]) S1 S2 1 20 200 add .count() if you just want the counts. More on reddit.com
1
1
January 7, 202409:01
How to Create Conditional Columns in Pandas | np.where() & ...
12:34
How to Filter Pandas Dataframe (subset rows and columns by value) ...
Set Pandas Conditional Column Based on Values of Another ...
12:23
How To Select Rows and columns in Pandas Data Frame, based on ...
- YouTube
09:20
Conditionals [IF / ELSE] in Pandas - create columns based on ...
Statology
statology.org › home › pandas: how to select columns based on condition
Pandas: How to Select Columns Based on Condition
November 4, 2022 - Method 3: Select Columns Where At Least One Row Meets Multiple Conditions · #select columns where at least one row has a value between 10 and 15 df.loc[:, ((df>=10) & (df<=15)).any()] The following examples show how to use each method with the following pandas DataFrame:
Spark By {Examples}
sparkbyexamples.com › home › pandas › select pandas columns based on condition
Select Pandas Columns Based on Condition - Spark By {Examples}
June 27, 2025 - We can select columns based on single/multiple conditions using the pandas loc[] attribute. The DataFrame.loc[] attribute property is used to select rows
GeeksforGeeks
geeksforgeeks.org › python › selecting-rows-in-pandas-dataframe-based-on-conditions
Selecting rows in pandas DataFrame based on conditions - GeeksforGeeks
October 30, 2025 - You can filter rows based on whether a column’s value exists in a list. ... Returns a boolean Series used to filter rows. ... Note: Always use parentheses around each condition. ... Age >= 20 and Stream in @options selects rows where Age ≥ 20 and Stream is in the Python list options.
Net Informations
net-informations.com › ds › pd › mcolumns.htm
Selecting multiple columns in a Pandas dataframe based on condition
This function is particularly useful for filtering data based on specific criteria and identifying rows that match certain conditions. ... The isin() method in Pandas enables the selection of multiple columns from a DataFrame based on specific conditional values.
KeyToDataScience
keytodatascience.com › data science › selecting rows and columns based on conditions in python pandas dataframe
Selecting Rows and Columns Based on Conditions in Python Pandas DataFrame - KeyToDataScience
January 16, 2022 - Select rows or columns in Pandas DataFrame based on various conditions using .loc, .iloc and conditional operators '>', '=', '!' With Examples and Code.
YouTube
youtube.com › watch
How to Select Columns Based on a Logical Condition in Pandas (Python) - YouTube
↓ Code Available Below! ↓ This video shows how to select columns of a data frame based on a logical condition. Filtering or subsetting the columns of a dat...
Published: October 29, 2020
Medium
medium.com › @heyamit10 › how-to-filter-columns-based-on-conditions-5f4c41dac45b
How to Filter Columns Based on Conditions? | by Hey Amit | Medium
March 6, 2025 - Instead of hardcoding, just pass the list to pandas.” · columns_to_keep = ['Name', 'Salary'] df_filtered = df[columns_to_keep] print(df_filtered) ... If you’re working with different datasets but need consistent columns, just update the list. No need for repetitive manual selection.
Codegive
codegive.com › blog › pandas_select_columns_by_condition.php
Master Pandas Column Selection: Unleash Data Insights by Condition in 2026!
A: df.filter() is a specialized method optimized for selecting columns (or rows) based on labels (names) using like (substring) or regex (regular expression) patterns. df.loc is a general label-based indexer that, when combined with a boolean array derived from df.columns or df.dtypes, offers maximum flexibility to select columns by condition using arbitrary logic. For simple name patterns, filter is often more concise; for complex or data-dependent conditions, loc with boolean indexing is necessary. Q: How do I select columns that contain no missing values in pandas?
Reddit
reddit.com › r/learnpython › selecting columns based on condition
r/learnpython on Reddit: Selecting Columns Based on Condition
August 7, 2023 -
Hey, everyone! This should be super easy and I think I'm having Monday-brain, but I'm having trouble trying to trying to do something to rows based on particular conditions. Here's some pseudo data:
d = {'Col 1': ['a', 'b'], 'Col 1 %': [1, 2], 'Col 2': ['c', 'd'], 'Col 2 %': [3, 4], 'Col 3': ['e', 'f']'Col 3 %': [2, 4]}
fake_df = pd.DataFrame(data=d)
fake_dfThe df looks like...
Col 1 Col 1 % Col 2 Col 2 % Col 3 Col 3 % 0 a 1 c 3 e 2 1 b 2 d 4 f 4
Now let's say I want to do a sum across all rows. Now it won't work by doing sum because not all of these are ints/floats. What I'm currently doing to do this is:
fake_df['Total'] = fake_df.columns[:-1] == "%".sum()
The error is:
AttributeError: 'str' object has no attribute 'sum'
Now I get it. I know it's not working, but Google isn't really too kind because when I search for the answer, it's thinking I'm searching based on some of the values within the data, not the columns. My intended solution is something like:
Total 0 6 1 10
Now for the hell of it, I tried:
fake_df['Total'] = fake_df.sum()
Which, as expected, resulted in:
Col 1 Col 1 % Col 2 Col 2 % Col 3 Col 3 % Total 0 a 1 c 3 e 2 NaN 1 b 2 d 4 f 4 NaN
I'm going to ask that the response not use any argument within the sum function to skip that because in my actual df problem, I only want to use rows that end in "%", and not all numeric-only columns end in that.
Thanks!
Top answer 1 of 2
2
If you don't want to do numeric_only because other columns also contain numbers, you can filter columns by name. As all the relevant columns have % in you can do df['total'] = df.filter(regex="%").sum(axis=1) Or you can pass in a list of column names to filter instead if you can't regex it. cols = ["Col 1 %", "Col 2 %", "Col 3 %"] df['total'] = df[cols].sum(axis=1)
2 of 2
1
No, that error has nothing to do with dataframes; it is that you have asked Python to do literally '%'.sum(), which clearly doesn't exist. I suspect you intended something like fake_df[fake_df.columns[:-1] == "%"].sum() but that is not the right syntax either; that would attempt to filter the rows of the df by the column names, which makes no sense. Instead you should do this in two steps: filter the column names to those ending in % (and for which you need the str accessor), then filter the df by those columns and sum them (again, note you need the axis=1 to sum by rows not columns): cols = fake_df.columns[fake_df.columns.str[-1] == "%"] fake_df[cols].sum(axis=1) But actually all this is unnecessary, as sum has an argument to sum numeric only: fake_df.sum(axis=1, numeric_only=True)
Codepointtech
codepointtech.com › home › how to select columns based on condition in pandas
How to Select Columns Based on Condition in Pandas - codepointtech.com
January 17, 2026 - One of the most common conditions for column selection is their data type. Pandas provides the convenient select_dtypes() method for this purpose.
Towards Data Science
towardsdatascience.com › home › latest › interesting ways to select pandas dataframe columns
Interesting Ways to Select Pandas DataFrame Columns | Towards Data Science
April 16, 2021 - Here, if the mean of all the values in a column meet a condition, return the column. ... Thanks for checking this out and feel free to reference it often. ... A review on MIT Open Courseware - 6.0001: Introduction to Computer Science and Programming in Python (with my notes shared) ... An overview of Python's alternative to loops and why you should use them. ... A step-by-step guide to understanding distributed data, lazy logic, and your first DataFrame.
GeeksforGeeks
geeksforgeeks.org › pandas › pandas-select-columns
Pandas Select Columns - GeeksforGeeks
July 23, 2025 - This is helpful when you know the position of the columns rather than their names. ... The filter() method is useful when you want to select columns based on certain conditions, such as column names that match a specific pattern.
Statology
statology.org › home › pandas: how to select rows based on column values
Pandas: How to Select Rows Based on Column Values
March 25, 2025 - This approach allows you to filter ... complex conditions that depend on the data itself. You can apply custom functions using lambda expressions to implement sophisticated filtering logic. When working with pandas DataFrames, selecting and filtering rows based on column values is ...
datagy
datagy.io › home › pandas tutorials › viewing and selecting data in pandas › selecting columns in pandas: complete guide
Selecting Columns in Pandas: Complete Guide • datagy
December 15, 2022 - In this tutorial, you’ll learn how to select all the different ways you can select columns in Pandas, either by name or index. You’ll learn how to use the loc, iloc accessors and how to select columns directly. You’ll also learn how to select columns conditionally, such as those containing a specific substring.
Altcademy
altcademy.com › blog › how-to-select-certain-columns-in-pandas
How to select certain columns in Pandas - Altcademy.com
January 16, 2024 - In this example, df.mean() > 1 creates a boolean mask, where only columns satisfying the condition have True values. loc then uses this mask to select the appropriate columns. Let's use an analogy to reinforce what we've learned. Imagine a bookshelf representing your DataFrame. Each book on the shelf is a column. Using square brackets to select a column is like picking a specific book by its title.
Medium
medium.com › @arsalan_zafar › conditional-selection-in-pandas-f4a840960769
Conditional selection in Pandas. Part 5 — Pandas What Why How | by Arsalan Zafar | Medium
January 4, 2022 - We can also pass conditions with the loc and iloc methods, which is a more convenient way to filter out the data because we can get only those rows & columns we are interested. This time we create the same condition but assign it in a variable (high_salary_filter) that increases the readability, and our code looks better. ... Now we will pass our condition to the loc method and select only three columns — Country , ConvertedComp, LanguageWorkedWith
APXML
apxml.com › courses › essential-numpy-pandas › chapter-7-data-selection-indexing-pandas › conditional-selection
Conditional Selection (Boolean Indexing)
We can apply a comparison directly to the 'Age' column: ... As you can see, Pandas performs the comparison (> 25) element-wise for the entire 'Age' Series, resulting in a new Series of boolean values. True indicates the condition is met for that row, and False indicates it is not. This boolean Series acts like a filter. You can pass it directly inside the square brackets [] of the DataFrame (or .loc) to select ...
Top answer 1 of 3
8
Use gt and any to filter the df:
In [287]:
df.ix[:,df.gt(2).any()]
Out[287]:
2
0 1.590124
1 2.500397
Here we use ix to select all rows, the first : and the next arg is a boolean mask of the columns that meet the condition:
In [288]:
df.gt(2)
Out[288]:
0 1 2 3
0 False False False False
1 False False True False
In [289]:
df.gt(2).any()
Out[289]:
0 False
1 False
2 True
3 False
dtype: bool
In your example what you did was select the cell value for the first row and second column, you then tried to use this to mask the columns but this just returned the first column hence why it didn't work:
In [291]:
df.iloc[(0,1)]
Out[291]:
1.3296030000000001
In [293]:
df.columns[df.iloc[(0,1)]>2]
Out[293]:
'0'
2 of 3
3
Use mask created with df > 2 with any and then select columns by ix:
import pandas as pd
np.random.seed(18)
df = pd.DataFrame(np.random.randn(2, 4))
print(df)
0 1 2 3
0 0.079428 2.190202 -0.134892 0.160518
1 0.442698 0.623391 1.008903 0.394249
print ((df>2).any())
0 False
1 True
2 False
3 False
dtype: bool
print (df.ix[:, (df>2).any()])
1
0 2.190202
1 0.623391
EDIT by comment:
You can check your solution per partes:
It seems it works, but it always select second column (1, python count from 0) column if condition True:
print (df.iloc[(0,1)])
2.19020235741
print (df.iloc[(0,1)] > 2)
True
print (df.columns[df.iloc[(0,1)]>2])
1
print (df[df.columns[df.iloc[(0,1)]>2]])
0 2.190202
1 0.623391
Name: 1, dtype: float64
And first column (0) column if False, because boolean True and False are casted to 1 and 0:
np.random.seed(15)
df = pd.DataFrame(np.random.randn(2, 4))
print (df)
0 1 2 3
0 -0.312328 0.339285 -0.155909 -0.501790
1 0.235569 -1.763605 -1.095862 -1.087766
print (df.iloc[(0,1)])
0.339284706046
print (df.iloc[(0,1)] > 2)
False
print (df.columns[df.iloc[(0,1)]>2])
0
print (df[df.columns[df.iloc[(0,1)]>2]])
0 -0.312328
1 0.235569
Name: 0, dtype: float64
If change column names:
np.random.seed(15)
df = pd.DataFrame(np.random.randn(2, 4))
df.columns = ['a','b','c','d']
print (df)
a b c d
0 -0.312328 0.339285 -0.155909 -0.501790
1 0.235569 -1.763605 -1.095862 -1.087766
print (df.iloc[(0,1)] > 2)
False
print (df[df.columns[df.iloc[(0,1)]>2]])
0 -0.312328
1 0.235569
Name: a, dtype: float64