> For the complete documentation index, see [llms.txt](https://igb.mit.edu/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://igb.mit.edu/mini-courses/python/data-processing-with-python/pandas/selecting-from-dataframes.md). # Selecting from DataFrames You can "search/select" data by generating "boolean" arrays based on some criteria. This works by effectively generating a column of True/False values that Pandas uses to select particular rows (those that are true). There are a few ways to generate this true/false selection column. ### **Value-based selections** You provide a selection criteria for a particular column. Example: 
# generates the true/false array
my_dataframe['my_column']>=some_value
### **Is-in based selections** You provide a list of values you want to search for. Example: ``` subset_of_rows = my_dataframe['column_name'].isin([list_of_values]) ``` ### **Other** There are lots of ways to do this - you can learn more [here](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#indexing-boolean) ### Boolean Indexing ``` ms['Precursor Charge']==3 ``` This is boolean indexing - you can make very complicated selection criteria to just pull out the data you want ``` selection_criteria = ms['Precursor Charge']==3 #now we have saved the selection criteria ``` ``` selection_criteria ```
``` ms[selection_criteria] #note that only the "True" rows are selected ```
``` ms[ms['Precursor Charge']==3] ```
``` # Try to select all of the rows with "light Precursor Mz" greater than 800, and do it in one line. ms[ms['light Precursor Mz']>800] ```
``` ms[ms['Peptide Modified Sequence'].str.contains('Q')][['Protein Preferred Name', 'Peptide Modified Sequence']] ```
``` ms[ms['Peptide Modified Sequence'].str.contains('SV')] ```
``` # Edit the above to only get peptides with the motif 'SV' and only output interested columns ms[ms['Peptide Modified Sequence'].str.contains('SV')][['Protein Preferred Name', 'Peptide Modified Sequence']] ```
``` # now let's try using "isin" ms[ms['Protein Preferred Name'].isin(['RL27_ECOLI'])] ```
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