12/24/21, 1:52 PM Spark DataFrames Project Exercise - Jupyter Notebook

Spark DataFrames Project Exercise

Let's get some quick practice with your new Spark DataFrame skills, you will be asked some basic questions
about some stock market data, in this case Walmart Stock from the years 2012-2017. This exercise will just ask
a bunch of questions, unlike the future machine learning exercises, which will be a little looser and be in the
form of "Consulting Projects", but more on that later!

For now, just answer the questions and complete the tasks below.

Use the walmart_stock.csv file to Answer and complete the tasks below!

Start a simple Spark Session

In [1]:

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName('walmart').getOrCreate()

Load the Walmart Stock CSV File, have Spark infer the data types.

In [2]:

df = spark.read.csv('walmart_stock.csv', inferSchema=True, header=True)

What are the column names?

In [3]:

df.columns

Out[3]:

['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Adj Close']

What does the Schema look like?

In [5]:

df.printSchema()

root

|-- Date: string (nullable = true)

|-- Open: double (nullable = true)

|-- High: double (nullable = true)

|-- Low: double (nullable = true)

|-- Close: double (nullable = true)

|-- Volume: integer (nullable = true)

|-- Adj Close: double (nullable = true)

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Print out the first 5 columns.

In [8]:

for line in df.head(5):

print(line,'\n')

Row(Date='2012-01-03', Open=59.970001, High=61.060001, Low=59.869999, Close=

60.330002, Volume=12668800, Adj Close=52.619234999999996)

Row(Date='2012-01-04', Open=60.209998999999996, High=60.349998, Low=59.47000

1, Close=59.709998999999996, Volume=9593300, Adj Close=52.078475)

Row(Date='2012-01-05', Open=59.349998, High=59.619999, Low=58.369999, Close=

59.419998, Volume=12768200, Adj Close=51.825539)

Row(Date='2012-01-06', Open=59.419998, High=59.450001, Low=58.869999, Close=

59.0, Volume=8069400, Adj Close=51.45922)

Row(Date='2012-01-09', Open=59.029999, High=59.549999, Low=58.919998, Close=

59.18, Volume=6679300, Adj Close=51.616215000000004)

Use describe() to learn about the DataFrame.

In [10]:

df.describe().show()

+-------+----------+------------------+-----------------+-----------------+-
----------------+-----------------+-----------------+

|summary| Date| Open| High| Low|

Close| Volume| Adj Close|

+-------+----------+------------------+-----------------+-----------------+-
----------------+-----------------+-----------------+

| count| 1258| 1258| 1258| 1258|

1258| 1258| 1258|

| mean| null| 72.35785375357709|72.83938807631165| 71.9186009594594|7

2.38844998012726|8222093.481717011|67.23883848728146|

| stddev| null| 6.76809024470826|6.768186808159218|6.744075756255496|

6.756859163732991| 4519780.8431556|6.722609449996857|

| min|2012-01-03|56.389998999999996| 57.060001| 56.299999|

56.419998| 2094900| 50.363689|

| max|2016-12-30| 90.800003| 90.970001| 89.25|

90.470001| 80898100|84.91421600000001|

+-------+----------+------------------+-----------------+-----------------+-
----------------+-----------------+-----------------+

Bonus Question!

There are too many decimal places for mean and stddev in the describe() dataframe. Format the
numbers to just show up to two decimal places. Pay careful attention to the datatypes that .describe()
returns, we didn't cover how to do this exact formatting, but we covered something very similar. Check
this link for a hint
(http://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.Column.cast)

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If you get stuck on this, don't worry, just view the solutions.

In [18]:

from pyspark.sql.types import (StructField, StringType,

IntegerType, StructType)
data_schema = [StructField('summary', StringType(), True),
StructField('Open', StringType(), True),
StructField('High', StringType(), True),
StructField('Low', StringType(), True),
StructField('Close', StringType(), True),
StructField('Volume', StringType(), True),
StructField('Adj Close', StringType(), True)
]
final_struc = StructType(fields=data_schema)

df = spark.read.csv('walmart_stock.csv', inferSchema=True, header=True)

In [19]:

df.printSchema()

root

|-- Date: string (nullable = true)

|-- Open: double (nullable = true)

|-- High: double (nullable = true)

|-- Low: double (nullable = true)

|-- Close: double (nullable = true)

|-- Volume: integer (nullable = true)

|-- Adj Close: double (nullable = true)

In [22]:

from pyspark.sql.functions import format_number

summary = df.describe()
summary.select(summary['summary'],
format_number(summary['Open'].cast('float'), 2).alias('Ope
format_number(summary['High'].cast('float'), 2).alias('Hig
format_number(summary['Low'].cast('float'), 2).alias('Low'
format_number(summary['Close'].cast('float'), 2).alias('Cl
format_number(summary['Volume'].cast('int'),0).alias('Volu
).show()

+-------+--------+--------+--------+--------+----------+

|summary| Open| High| Low| Close| Volume|

+-------+--------+--------+--------+--------+----------+

| count|1,258.00|1,258.00|1,258.00|1,258.00| 1,258|

| mean| 72.36| 72.84| 71.92| 72.39| 8,222,093|

| stddev| 6.77| 6.77| 6.74| 6.76| 4,519,780|

| min| 56.39| 57.06| 56.30| 56.42| 2,094,900|

| max| 90.80| 90.97| 89.25| 90.47|80,898,100|

+-------+--------+--------+--------+--------+----------+

Create a new dataframe with a column called HV Ratio that is the ratio of the High Price versus volume
of stock traded for a day.

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In [23]:

df_hv = df.withColumn('HV Ratio', df['High']/df['Volume']).select(['HV Ratio'])

df_hv.show()

+--------------------+

| HV Ratio|

+--------------------+

|4.819714653321546E-6|

|6.290848613094555E-6|

|4.669412994783916E-6|

|7.367338463826307E-6|

|8.915604778943901E-6|

|8.644477436914568E-6|

|9.351828421515645E-6|

| 8.29141562102703E-6|

|7.712212102001476E-6|

|7.071764823529412E-6|

|1.015495466386981E-5|

|6.576354146362592...|

| 5.90145296180676E-6|

|8.547679455011844E-6|

|8.420709512685392E-6|

|1.041448341728929...|

|8.316075414862431E-6|

|9.721183814992126E-6|

|8.029436027707578E-6|

|6.307432259386365E-6|

+--------------------+

only showing top 20 rows

What day had the Peak High in Price?

In [25]:

df.orderBy(df['High'].desc()).select(['Date']).head(1)[0]['Date']

Out[25]:

'2015-01-13'

What is the mean of the Close column?

In [26]:

from pyspark.sql.functions import mean

df.select(mean('Close')).show()

+-----------------+

| avg(Close)|

+-----------------+

|72.38844998012726|

+-----------------+

What is the max and min of the Volume column?

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In [27]:

from pyspark.sql.functions import min, max

In [28]:

df.select(max('Volume'),min('Volume')).show()

+-----------+-----------+

|max(Volume)|min(Volume)|

+-----------+-----------+

| 80898100| 2094900|

+-----------+-----------+

How many days was the Close lower than 60 dollars?

In [29]:

df.filter(df['Close'] < 60).count()

Out[29]:

81

What percentage of the time was the High greater than 80 dollars ?

In other words, (Number of Days High>80)/(Total Days in the dataset)

In [107]:

df.filter('High > 80').count() * 100/df.count()

Out[107]:

9.141494435612083

What is the Pearson correlation between High and Volume?

Hint
(http://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrameStatFunctions.co

In [31]:

df.corr('High', 'Volume')

+-------------------+

| corr(High, Volume)|

+-------------------+

|-0.3384326061737161|

+-------------------+

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What is the max High per year?

In [32]:

from pyspark.sql.functions import (dayofmonth, hour,

dayofyear, month,
year, weekofyear,
format_number, date_format)
year_df = df.withColumn('Year', year(df['Date']))
year_df.groupBy('Year').max()['Year', 'max(High)'].show()

+----+---------+

|Year|max(High)|

+----+---------+

|2015|90.970001|

|2013|81.370003|

|2014|88.089996|

|2012|77.599998|

|2016|75.190002|

+----+---------+

What is the average Close for each Calendar Month?

In other words, across all the years, what is the average Close price for Jan,Feb, Mar, etc... Your result
will have a value for each of these months.

In [33]:

month_df = df.withColumn('Month', month(df['Date']))

month_df = month_df.groupBy('Month').mean()

month_df = month_df.orderBy('Month')

month_df['Month', 'avg(Close)'].show()

+-----+-----------------+

|Month| avg(Close)|

+-----+-----------------+

| 1|71.44801958415842|

| 2| 71.306804443299|

| 3|71.77794377570092|

| 4|72.97361900952382|

| 5|72.30971688679247|

| 6| 72.4953774245283|

| 7|74.43971943925233|

| 8|73.02981855454546|

| 9|72.18411785294116|

| 10|71.57854545454543|

| 11| 72.1110893069307|

| 12|72.84792478301885|

+-----+-----------------+

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Great Job!

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