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content/blog/iterativecsv.md

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+---
+title: "Iteratively Read CSV"
+date: 2020-04-11T21:34:33-04:00
+draft: false
+tags: ["python"]
+---
+
+If you want to analyze a CSV dataset that is larger than the space available in RAM, then you can iteratively process each observation and store/calculate only what you need. There is a way to do this in standard Python as well as the popular library Pandas.
+
+## Standard Library
+
+ ```python
+import csv
+with open('/path/to/data.csv', newline='') as csvfile:
+    reader = csv.reader(csvfile, delimeter=',')
+    for row in reader:
+        for column in row:
+            do_something()
+ ```
+
+## Pandas
+
+Pandas is slightly different in where you specify a `chunksize` which is the number of rows per chunk and you get a pandas dataframe with that many rows
+
+```python
+import pandas as pd
+chunksize = 100
+for chunk in pd.read_csv('/path/to/data.csv', chunksize=chunksize):
+    do_something(chunk)
+```
+