Bodo Python Quickstart (Local)¶
This quickstart guide will walk you through the process of running a simple Python computation using Bodo on your local machine.
Prerequisites¶
First, you need to install Bodo. You can install the Community Edition using conda, which allows you to use Bodo for free on up to 8 cores.
Generate Sample Data¶
Let's start by creating a parquet file with some sample data. The following Python code creates a parquet file with two columns A and B and 20 million rows. The column A contains values from 0 to 29, and the column B contains values from 0 to 19,999,999.
import pandas as pd
import numpy as np
import bodo
import time
NUM_GROUPS = 30
NUM_ROWS = 20_000_000
df = pd.DataFrame({
"A": np.arange(NUM_ROWS) % NUM_GROUPS,
"B": np.arange(NUM_ROWS)
})
df.to_parquet("my_data.pq")
A Simple Pandas Computation¶
Now let's write a simple Python function that computes the sum of column A for all rows where B is greater than 4 using pandas. We need to decorate the function with @bodo.jit to indicate that we want to compile the code using Bodo. Let's also add a timer to measure the execution time.
@bodo.jit(cache=True)
def computation():
t1 = time.time()
df = pd.read_parquet("my_data.pq")
df1 = df[df.B > 4].A.sum()
print("Execution time:", time.time() - t1)
return df1
result = computation()
print(result)
Running the Code¶
Bringing it all together, the complete code looks like this:
import pandas as pd
import numpy as np
import bodo
import time
NUM_GROUPS = 30
NUM_ROWS = 20_000_000
df = pd.DataFrame({
"A": np.arange(NUM_ROWS) % NUM_GROUPS,
"B": np.arange(NUM_ROWS)
})
df.to_parquet("my_data.pq")
@bodo.jit(cache=True)
def computation():
t1 = time.time()
df = pd.read_parquet("my_data.pq")
df1 = df[df.B > 4].A.sum()
print("Execution time:", time.time() - t1)
return df1
result = computation()
print(result)
To run the code, save it to a file, e.g. test_bodo.py, and run the following command in your terminal:
Replace 8 with the number of cores you want to use. Note that the first time you run this code, it may take a few seconds to compile the code. Next time you run the code, it will execute much faster. Check the Python API Reference for the full list of supported Python operations.