# 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:

<pre><code># generates the true/false array
<strong>my_dataframe['my_column']>=some_value
</strong></code></pre>

### **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
```

<figure><img src="/files/aNk7EjP3UiWYsDGXLHCf" alt=""><figcaption></figcaption></figure>

```
ms[selection_criteria] #note that only the "True" rows are selected
```

<figure><img src="/files/Zvbqk9GBAQIxGqYFAnlw" alt=""><figcaption></figcaption></figure>

```
ms[ms['Precursor Charge']==3]
```

<figure><img src="/files/VTp3s4L7tGfgafdqjhQG" alt=""><figcaption></figcaption></figure>

```
# 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]
```

<figure><img src="/files/B5vii9vF5hsQkBtUR10e" alt=""><figcaption></figcaption></figure>

```
ms[ms['Peptide Modified Sequence'].str.contains('Q')][['Protein Preferred Name', 'Peptide Modified Sequence']]
```

<figure><img src="/files/bcGenBAxq13IQV5Jtkrd" alt=""><figcaption></figcaption></figure>

```
ms[ms['Peptide Modified Sequence'].str.contains('SV')]
```

<figure><img src="/files/DLk5VqEmb0LEGrOUb2yU" alt=""><figcaption></figcaption></figure>

```
# 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']]
```

<figure><img src="/files/0If1SvtNPcTmYZOy6BU9" alt=""><figcaption></figcaption></figure>

```
# now let's try using "isin"
ms[ms['Protein Preferred Name'].isin(['RL27_ECOLI'])]
```

<figure><img src="/files/bx0wPhvtoe1z7JWlG9EL" alt=""><figcaption></figcaption></figure>


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