# 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'])]
```
