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package recfun

import scala.collection.mutable.ListBuffer

object Main {
  def main(args: Array[String]) {
    println("Pascal's Triangle")
    for (row <- 0 to 10) {
      for (col <- 0 to row)
        print(pascal(col, row) + " ")

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from sklearn.neural_network import MLPClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import RBF
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis

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reports = {}

for name, clf in zip(classifier_names, classifiers):
    clf.fit(X_train, Y_train)
    Y_predictions = clf.predict(X_test)
    Y_true = Y_test
    reports[name] = classification_report(Y_true, Y_predictions, output_dict=True)

reporting_df = pd.DataFrame.from_dict({(i,j): reports[i][j] 
                           for i in reports.keys() 

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from collections import defaultdict

r_dd = lambda: defaultdict(r_dd)

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import boto3
import botocore
from joblib import Parallel, delayed
import os

bucket_name = "bucket_name"
s3 = boto3.resource('s3')
bucket = s3.Bucket(bucketname)
exists = True
try:

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import foo
import argparse

def main(quux, quuz, **kwargs):
  try:
    if quux is True:
      foo.bar()
    else:
      foo.baz()
  except Exception as ex:

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import os
import csv
import argparse
from tqdm import tqdm

def split(filehandler, delimiter=',', row_limit=10000,
    output_name_template='output_%s.csv', output_path='.', keep_headers=True):
    """
    Splits a CSV file into multiple pieces.

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import os
import tempfile
import uuid
import argparse

import boto3
import azure.storage.blob as blob
import tqdm

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import geopandas as gpd
from shapely.ops import nearest_points 

def nearest(row, df1, df2, geom1_col='geometry', geom2_col='geometry', src_column=None):
    """Find the nearest point and return the corresponding value from specified column."""
    # Construct a multipoint object
    geom_union = df2.unary_union
    # Find the geometry that is closest
    nearest = df2[geom2_col] == nearest_points(row[geom1_col], geom_union)[1]
    # Get the corresponding value from df2 (matching is based on the geometry)

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import pandas as pd
from pyproj import Transformer


def towgs84(row, lat_col, lon_col):
  """An apply function for a pyproj transformer from 4326/WGS84 to 3857/web mercator
  
  :param row: a row of a pandas dataframe as supplied by df.apply().
  :param lat_col: str of the lat col
  :param lon_col: str of the lon col