fyyying

## titanic_dataset.csv

          
            PassengerId
            Survived
            Pclass
            Name
            Sex
            Age
            SibSp
            Parch
            Ticket
            Fare
            Cabin
            Embarked

            
              1
              0
              3
              Braund, Mr. Owen Harris
              male
              22.0
              1
              0
              A/5 21171
              7.25
              
              S

            
              2
              1
              1
              Cumings, Mrs. John Bradley (Florence Briggs Thayer)
              female
              38.0
              1
              0
              PC 17599
              71.2833
              C85
              C

            
              3
              1
              3
              Heikkinen, Miss. Laina
              female
              26.0
              0
              0
              STON/O2. 3101282
              7.925
              
              S

            
              4
              1
              1
              Futrelle, Mrs. Jacques Heath (Lily May Peel)
              female
              35.0
              1
              0
              113803
              53.1
              C123
              S

            
              5
              0
              3
              Allen, Mr. William Henry
              male
              35.0
              0
              0
              373450
              8.05
              
              S

            
              6
              0
              3
              Moran, Mr. James
              male
              
              0
              0
              330877
              8.4583
              
              Q

            
              7
              0
              1
              McCarthy, Mr. Timothy J
              male
              54.0
              0
              0
              17463
              51.8625
              E46
              S

            
              8
              0
              3
              Palsson, Master. Gosta Leonard
              male
              2.0
              3
              1
              349909
              21.075
              
              S

            
              9
              1
              3
              Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)
              female
              27.0
              0
              2
              347742
              11.1333
              
              S

## create_empty_df.py
from pyspark.sql.types import StructType

# Create an empty DataFrame with empty schema
schema = StructType([])
spark.createDataFrame(spark.sparkContext.emptyRDD(), schema)

## create_df_with_sc.py
# Create a small dataset with SparkContext

data = ["Owen", 22]
rdd = spark.sparkContext.parallelize(data)
df = spark.createDataFrame(rdd, ["name", "age"])

## create_df_with_sc.py
# Create a small dataset with SparkContext

data = ["Owen", 22}]
rdd = spark.sparkContext.parallelize(data)
df = spark.createDataFrame(rdd, ["name", "age"])

## discounted_cashflow_pandas.py
# Implementation with Pandas
import pandas as pd

# Read in the cash flows data and rate data as csv
cashflow_df = pd.read_csv(path_cashflow)
rate_df = pd.read_csv(path_rate)

# Calculate discount factor from the rates
rate_df["Discount factor"] = 1 / (1 + rate_df["Interest rate"])**rate_df["Year"]

## __ini__.py
import logging
import os
import azure.functions as func
import json
import stripe


# This is your real test secret API key.
stripe.api_key = os.environ["STRIPE_API_KEY"]

## CheckoutForm.jsx
import React, { useState, useEffect } from "react";
import {
  CardElement,
  useStripe,
  useElements
} from "@stripe/react-stripe-js";

export default function CheckoutForm() {
  const [succeeded, setSucceeded] = useState(false);
  const [error, setError] = useState(null);

## pyspark_read_dataset.py
# Path on gist
path = "https://gist.githubusercontent.com/fyyying/4aa5b471860321d7b47fd881898162b7/raw/e8606de9a82e13ca6215b340ce260dad60469cba/titanic_dataset.csv"

# Read from local
df = spark.read.csv("titanic_dataset.csv", header=True, inferSchema=True)

# Read from url
# One more step required to add the url into file
spark.sparkContext.addFile(path)
df = spark.read.csv(SparkFiles.get("titanic_dataset.csv"), header=True, inferSchema=True)

## pyspark_read_dataset_all_formats.py
path = "https://gist.githubusercontent.com/fyyying/4aa5b471860321d7b47fd881898162b7/raw/e8606de9a82e13ca6215b340ce260dad60469cba/titanic_dataset.csv"

# read in the csv file
df = spark.read.format('csv').load(SparkFiles.get("titanic_dataset.csv"), header=True, inferSchema=True)

# One can read in data from csv/partquet/json... if the path is linked to a parquet or json file
df = spark.read.format('json').load(SparkFiles.get("titanic_dataset.json"), header=True, inferSchema=True)
df = spark.read.format('parquet').load(SparkFiles.get("titanic_dataset.parquet"), header=True, inferSchema=True)

## pyspark_read_dataset_from_pandas.py
# Read data from a pandas dataframe

path = "https://gist.githubusercontent.com/fyyying/4aa5b471860321d7b47fd881898162b7/raw/e8606de9a82e13ca6215b340ce260dad60469cba/titanic_dataset.csv"
# Be careful the object type in pandas can not be understood
# Explicitly change to string type
pd_df = pd.read_csv(path)
df = spark.createDataFrame(pd_df)
PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.25		S
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Thayer)	female	38.0	1	0	PC 17599	71.2833	C85	C
3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.925		S
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1	C123	S
5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.05		S
6	0	3	Moran, Mr. James	male		0	0	330877	8.4583		Q
7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S
8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.075		S
9	1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	347742	11.1333		S
	from pyspark.sql.types import StructType

	# Create an empty DataFrame with empty schema
	schema = StructType([])
	spark.createDataFrame(spark.sparkContext.emptyRDD(), schema)
	# Create a small dataset with SparkContext

	data = ["Owen", 22]
	rdd = spark.sparkContext.parallelize(data)
	df = spark.createDataFrame(rdd, ["name", "age"])
	# Create a small dataset with SparkContext

	data = ["Owen", 22}]
	rdd = spark.sparkContext.parallelize(data)
	df = spark.createDataFrame(rdd, ["name", "age"])
	# Implementation with Pandas
	import pandas as pd

	# Read in the cash flows data and rate data as csv
	cashflow_df = pd.read_csv(path_cashflow)
	rate_df = pd.read_csv(path_rate)

	# Calculate discount factor from the rates
	rate_df["Discount factor"] = 1 / (1 + rate_df["Interest rate"])**rate_df["Year"]
	import logging
	import os
	import azure.functions as func
	import json
	import stripe


	# This is your real test secret API key.
	stripe.api_key = os.environ["STRIPE_API_KEY"]
	import React, { useState, useEffect } from "react";
	import {
	CardElement,
	useStripe,
	useElements
	} from "@stripe/react-stripe-js";

	export default function CheckoutForm() {
	const [succeeded, setSucceeded] = useState(false);
	const [error, setError] = useState(null);
	# Path on gist
	path = "https://gist.githubusercontent.com/fyyying/4aa5b471860321d7b47fd881898162b7/raw/e8606de9a82e13ca6215b340ce260dad60469cba/titanic_dataset.csv"

	# Read from local
	df = spark.read.csv("titanic_dataset.csv", header=True, inferSchema=True)

	# Read from url
	# One more step required to add the url into file
	spark.sparkContext.addFile(path)
	df = spark.read.csv(SparkFiles.get("titanic_dataset.csv"), header=True, inferSchema=True)
	path = "https://gist.githubusercontent.com/fyyying/4aa5b471860321d7b47fd881898162b7/raw/e8606de9a82e13ca6215b340ce260dad60469cba/titanic_dataset.csv"

	# read in the csv file
	df = spark.read.format('csv').load(SparkFiles.get("titanic_dataset.csv"), header=True, inferSchema=True)

	# One can read in data from csv/partquet/json... if the path is linked to a parquet or json file
	df = spark.read.format('json').load(SparkFiles.get("titanic_dataset.json"), header=True, inferSchema=True)
	df = spark.read.format('parquet').load(SparkFiles.get("titanic_dataset.parquet"), header=True, inferSchema=True)
	# Read data from a pandas dataframe

	path = "https://gist.githubusercontent.com/fyyying/4aa5b471860321d7b47fd881898162b7/raw/e8606de9a82e13ca6215b340ce260dad60469cba/titanic_dataset.csv"
	# Be careful the object type in pandas can not be understood
	# Explicitly change to string type
	pd_df = pd.read_csv(path)
	df = spark.createDataFrame(pd_df)