diff --git a/ContainsNull.csv b/ContainsNull.csv
new file mode 100644
index 0000000000000000000000000000000000000000..6431d7a8421602ffe646307144ad3e4e79a018f8
--- /dev/null
+++ b/ContainsNull.csv
@@ -0,0 +1,5 @@
+Id,Name,Sales
+emp1,John,
+emp2,,
+emp3,,345.0
+emp4,Cindy,456.0
diff --git a/Spark basics.ipynb b/Spark basics.ipynb
index 245d5701c50546a4515e5bf497e1cdf455ce5dd8..f67702e1abb13c8b4d67802f9ba3f54bb7acf79e 100644
--- a/Spark basics.ipynb
+++ b/Spark basics.ipynb
@@ -27,6 +27,1477 @@
"df=spark.read.json('people.json')"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+----+-------+\n",
+ "| age| name|\n",
+ "+----+-------+\n",
+ "|null|Michael|\n",
+ "| 30| Andy|\n",
+ "| 19| Justin|\n",
+ "+----+-------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "root\n",
+ " |-- age: long (nullable = true)\n",
+ " |-- name: string (nullable = true)\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.printSchema()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['age', 'name']"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.columns\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "DataFrame[summary: string, age: string, name: string]"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+-------+------------------+-------+\n",
+ "|summary| age| name|\n",
+ "+-------+------------------+-------+\n",
+ "| count| 2| 3|\n",
+ "| mean| 24.5| null|\n",
+ "| stddev|7.7781745930520225| null|\n",
+ "| min| 19| Andy|\n",
+ "| max| 30|Michael|\n",
+ "+-------+------------------+-------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.describe().show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "if schema is not crt we can change it like if its integer or string"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pyspark.sql.types import StructField,StringType,IntegerType,StructType"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data_schema =[StructField('age',IntegerType(),True),\n",
+ " StructField('name',StringType(),True)]\n",
+ "#field,datatype to change to , is there null present =True or false"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "final_struct=StructType(fields=data_schema)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ " df=spark.read.json('people.json',schema=final_struct)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "root\n",
+ " |-- age: integer (nullable = true)\n",
+ " |-- name: string (nullable = true)\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.printSchema()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Column<'age'>"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# grab data from this like grab age vs selsct age\n",
+ "df['age']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "pyspark.sql.column.Column"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "type(df['age'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# that is a column 'Column' object is not callable..if i want dataframe we use select"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+----+\n",
+ "| age|\n",
+ "+----+\n",
+ "|null|\n",
+ "| 30|\n",
+ "| 19|\n",
+ "+----+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.select('age').show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[Row(age=None, name='Michael'), Row(age=30, name='Andy')]"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head(2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Row(age=None, name='Michael')"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head(2)[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# the above line is a row object"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+----+-------+\n",
+ "| age| name|\n",
+ "+----+-------+\n",
+ "|null|Michael|\n",
+ "| 30| Andy|\n",
+ "| 19| Justin|\n",
+ "+----+-------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.select(['age','name']).show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+----+-------+------+\n",
+ "| age| name|newage|\n",
+ "+----+-------+------+\n",
+ "|null|Michael| null|\n",
+ "| 30| Andy| 30|\n",
+ "| 19| Justin| 19|\n",
+ "+----+-------+------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# add a new column\n",
+ "df.withColumn('newage',df['age']).show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# a copy of age will be new age\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+----+-------+----------+\n",
+ "| age| name|double_age|\n",
+ "+----+-------+----------+\n",
+ "|null|Michael| null|\n",
+ "| 30| Andy| 60|\n",
+ "| 19| Justin| 38|\n",
+ "+----+-------+----------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.withColumn('double_age',df['age']*2).show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+----+-------+\n",
+ "| age| name|\n",
+ "+----+-------+\n",
+ "|null|Michael|\n",
+ "| 30| Andy|\n",
+ "| 19| Justin|\n",
+ "+----+-------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+----------+-------+\n",
+ "|my_new_age| name|\n",
+ "+----------+-------+\n",
+ "| null|Michael|\n",
+ "| 30| Andy|\n",
+ "| 19| Justin|\n",
+ "+----------+-------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.withColumnRenamed('age','my_new_age').show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pure sql to interact with the data frame"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# sql etmporary view"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.createOrReplaceTempView('people')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "results=spark.sql(\"SELECT * FROM people\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+----+-------+\n",
+ "| age| name|\n",
+ "+----+-------+\n",
+ "|null|Michael|\n",
+ "| 30| Andy|\n",
+ "| 19| Justin|\n",
+ "+----+-------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "results.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "results=spark.sql(\"SELECT * FROM people WHERE age=30\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+---+----+\n",
+ "|age|name|\n",
+ "+---+----+\n",
+ "| 30|Andy|\n",
+ "+---+----+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "results.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# new lesson on dataFrame"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "spark=SparkSession.builder.appName('ops').getOrCreate()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df=spark.read.csv('appl_stock.csv',inferSchema=True,header=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "root\n",
+ " |-- Date: string (nullable = true)\n",
+ " |-- Open: double (nullable = true)\n",
+ " |-- High: double (nullable = true)\n",
+ " |-- Low: double (nullable = true)\n",
+ " |-- Close: double (nullable = true)\n",
+ " |-- Volume: integer (nullable = true)\n",
+ " |-- Adj Close: double (nullable = true)\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.printSchema()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Row(Date='2010-01-04', Open=213.429998, High=214.499996, Low=212.38000099999996, Close=214.009998, Volume=123432400, Adj Close=27.727039)"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+----------+------------------+------------------+------------------+------------------+---------+------------------+\n",
+ "| Date| Open| High| Low| Close| Volume| Adj Close|\n",
+ "+----------+------------------+------------------+------------------+------------------+---------+------------------+\n",
+ "|2010-01-04| 213.429998| 214.499996|212.38000099999996| 214.009998|123432400| 27.727039|\n",
+ "|2010-01-05| 214.599998| 215.589994| 213.249994| 214.379993|150476200|27.774976000000002|\n",
+ "|2010-01-06| 214.379993| 215.23| 210.750004| 210.969995|138040000|27.333178000000004|\n",
+ "|2010-01-07| 211.75| 212.000006| 209.050005| 210.58|119282800| 27.28265|\n",
+ "|2010-01-08| 210.299994| 212.000006|209.06000500000002|211.98000499999998|111902700| 27.464034|\n",
+ "|2010-01-11|212.79999700000002| 213.000002| 208.450005|210.11000299999998|115557400| 27.221758|\n",
+ "|2010-01-12|209.18999499999998|209.76999500000002| 206.419998| 207.720001|148614900| 26.91211|\n",
+ "|2010-01-13| 207.870005|210.92999500000002| 204.099998| 210.650002|151473000| 27.29172|\n",
+ "|2010-01-14|210.11000299999998|210.45999700000002| 209.020004| 209.43|108223500| 27.133657|\n",
+ "|2010-01-15|210.92999500000002|211.59999700000003| 205.869999| 205.93|148516900|26.680197999999997|\n",
+ "|2010-01-19| 208.330002|215.18999900000003| 207.240004| 215.039995|182501900|27.860484999999997|\n",
+ "|2010-01-20| 214.910006| 215.549994| 209.500002| 211.73|153038200| 27.431644|\n",
+ "|2010-01-21| 212.079994|213.30999599999998| 207.210003| 208.069996|152038600| 26.957455|\n",
+ "|2010-01-22|206.78000600000001| 207.499996| 197.16| 197.75|220441900| 25.620401|\n",
+ "|2010-01-25|202.51000200000001| 204.699999| 200.190002| 203.070002|266424900|26.309658000000002|\n",
+ "|2010-01-26|205.95000100000001| 213.710005| 202.580004| 205.940001|466777500| 26.681494|\n",
+ "|2010-01-27| 206.849995| 210.58| 199.530001| 207.880005|430642100|26.932840000000002|\n",
+ "|2010-01-28| 204.930004| 205.500004| 198.699995| 199.289995|293375600|25.819922000000002|\n",
+ "|2010-01-29| 201.079996| 202.199995| 190.250002| 192.060003|311488100| 24.883208|\n",
+ "|2010-02-01|192.36999699999998| 196.0|191.29999899999999| 194.729998|187469100| 25.229131|\n",
+ "+----------+------------------+------------------+------------------+------------------+---------+------------------+\n",
+ "only showing top 20 rows\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+----------+------------------+------------------+------------------+------------------+---------+------------------+\n",
+ "| Date| Open| High| Low| Close| Volume| Adj Close|\n",
+ "+----------+------------------+------------------+------------------+------------------+---------+------------------+\n",
+ "|2010-01-04| 213.429998| 214.499996|212.38000099999996| 214.009998|123432400| 27.727039|\n",
+ "|2010-01-05| 214.599998| 215.589994| 213.249994| 214.379993|150476200|27.774976000000002|\n",
+ "|2010-01-06| 214.379993| 215.23| 210.750004| 210.969995|138040000|27.333178000000004|\n",
+ "|2010-01-07| 211.75| 212.000006| 209.050005| 210.58|119282800| 27.28265|\n",
+ "|2010-01-08| 210.299994| 212.000006|209.06000500000002|211.98000499999998|111902700| 27.464034|\n",
+ "|2010-01-11|212.79999700000002| 213.000002| 208.450005|210.11000299999998|115557400| 27.221758|\n",
+ "|2010-01-12|209.18999499999998|209.76999500000002| 206.419998| 207.720001|148614900| 26.91211|\n",
+ "|2010-01-13| 207.870005|210.92999500000002| 204.099998| 210.650002|151473000| 27.29172|\n",
+ "|2010-01-14|210.11000299999998|210.45999700000002| 209.020004| 209.43|108223500| 27.133657|\n",
+ "|2010-01-15|210.92999500000002|211.59999700000003| 205.869999| 205.93|148516900|26.680197999999997|\n",
+ "|2010-01-19| 208.330002|215.18999900000003| 207.240004| 215.039995|182501900|27.860484999999997|\n",
+ "|2010-01-20| 214.910006| 215.549994| 209.500002| 211.73|153038200| 27.431644|\n",
+ "|2010-01-21| 212.079994|213.30999599999998| 207.210003| 208.069996|152038600| 26.957455|\n",
+ "|2010-01-22|206.78000600000001| 207.499996| 197.16| 197.75|220441900| 25.620401|\n",
+ "|2010-01-25|202.51000200000001| 204.699999| 200.190002| 203.070002|266424900|26.309658000000002|\n",
+ "|2010-01-26|205.95000100000001| 213.710005| 202.580004| 205.940001|466777500| 26.681494|\n",
+ "|2010-01-27| 206.849995| 210.58| 199.530001| 207.880005|430642100|26.932840000000002|\n",
+ "|2010-01-28| 204.930004| 205.500004| 198.699995| 199.289995|293375600|25.819922000000002|\n",
+ "|2010-01-29| 201.079996| 202.199995| 190.250002| 192.060003|311488100| 24.883208|\n",
+ "|2010-02-01|192.36999699999998| 196.0|191.29999899999999| 194.729998|187469100| 25.229131|\n",
+ "+----------+------------------+------------------+------------------+------------------+---------+------------------+\n",
+ "only showing top 20 rows\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.filter(\"Close < 500\").show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+------------------+\n",
+ "| Open|\n",
+ "+------------------+\n",
+ "| 213.429998|\n",
+ "| 214.599998|\n",
+ "| 214.379993|\n",
+ "| 211.75|\n",
+ "| 210.299994|\n",
+ "|212.79999700000002|\n",
+ "|209.18999499999998|\n",
+ "| 207.870005|\n",
+ "|210.11000299999998|\n",
+ "|210.92999500000002|\n",
+ "| 208.330002|\n",
+ "| 214.910006|\n",
+ "| 212.079994|\n",
+ "|206.78000600000001|\n",
+ "|202.51000200000001|\n",
+ "|205.95000100000001|\n",
+ "| 206.849995|\n",
+ "| 204.930004|\n",
+ "| 201.079996|\n",
+ "|192.36999699999998|\n",
+ "+------------------+\n",
+ "only showing top 20 rows\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.filter(\"Close < 500\").select('Open').show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+------------------+------------------+\n",
+ "| Open| Close|\n",
+ "+------------------+------------------+\n",
+ "| 213.429998| 214.009998|\n",
+ "| 214.599998| 214.379993|\n",
+ "| 214.379993| 210.969995|\n",
+ "| 211.75| 210.58|\n",
+ "| 210.299994|211.98000499999998|\n",
+ "|212.79999700000002|210.11000299999998|\n",
+ "|209.18999499999998| 207.720001|\n",
+ "| 207.870005| 210.650002|\n",
+ "|210.11000299999998| 209.43|\n",
+ "|210.92999500000002| 205.93|\n",
+ "| 208.330002| 215.039995|\n",
+ "| 214.910006| 211.73|\n",
+ "| 212.079994| 208.069996|\n",
+ "|206.78000600000001| 197.75|\n",
+ "|202.51000200000001| 203.070002|\n",
+ "|205.95000100000001| 205.940001|\n",
+ "| 206.849995| 207.880005|\n",
+ "| 204.930004| 199.289995|\n",
+ "| 201.079996| 192.060003|\n",
+ "|192.36999699999998| 194.729998|\n",
+ "+------------------+------------------+\n",
+ "only showing top 20 rows\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.filter(\"Close < 500\").select(['Open','Close']).show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+---------+\n",
+ "| Volume|\n",
+ "+---------+\n",
+ "|123432400|\n",
+ "|150476200|\n",
+ "|138040000|\n",
+ "|119282800|\n",
+ "|111902700|\n",
+ "|115557400|\n",
+ "|148614900|\n",
+ "|151473000|\n",
+ "|108223500|\n",
+ "|148516900|\n",
+ "|182501900|\n",
+ "|153038200|\n",
+ "|152038600|\n",
+ "|220441900|\n",
+ "|266424900|\n",
+ "|466777500|\n",
+ "|430642100|\n",
+ "|293375600|\n",
+ "|311488100|\n",
+ "|187469100|\n",
+ "+---------+\n",
+ "only showing top 20 rows\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.filter(df['Close'] <500).select('Volume').show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+----------+------------------+----------+----------+----------+---------+------------------+\n",
+ "| Date| Open| High| Low| Close| Volume| Adj Close|\n",
+ "+----------+------------------+----------+----------+----------+---------+------------------+\n",
+ "|2010-01-22|206.78000600000001|207.499996| 197.16| 197.75|220441900| 25.620401|\n",
+ "|2010-01-28| 204.930004|205.500004|198.699995|199.289995|293375600|25.819922000000002|\n",
+ "|2010-01-29| 201.079996|202.199995|190.250002|192.060003|311488100| 24.883208|\n",
+ "+----------+------------------+----------+----------+----------+---------+------------------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.filter((df['Close'] <200) & (df['Open']>200)).show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+----------+------------------+------------------+------------------+------------------+---------+------------------+\n",
+ "| Date| Open| High| Low| Close| Volume| Adj Close|\n",
+ "+----------+------------------+------------------+------------------+------------------+---------+------------------+\n",
+ "|2010-02-01|192.36999699999998| 196.0|191.29999899999999| 194.729998|187469100| 25.229131|\n",
+ "|2010-02-02| 195.909998| 196.319994|193.37999299999998| 195.859997|174585600|25.375532999999997|\n",
+ "|2010-02-03| 195.169994| 200.200003| 194.420004| 199.229994|153832000|25.812148999999998|\n",
+ "|2010-02-04| 196.730003| 198.370001| 191.570005| 192.050003|189413000| 24.881912|\n",
+ "|2010-02-05|192.63000300000002| 196.0| 190.850002| 195.460001|212576700|25.323710000000002|\n",
+ "|2010-02-08| 195.690006|197.88000300000002| 193.999994|194.11999699999998|119567700| 25.1501|\n",
+ "|2010-02-09| 196.419996| 197.499994| 194.749998|196.19000400000002|158221700| 25.418289|\n",
+ "|2010-02-10| 195.889997| 196.6| 194.26|195.12000700000002| 92590400| 25.27966|\n",
+ "|2010-02-11| 194.880001| 199.750006|194.05999599999998| 198.669994|137586400| 25.739595|\n",
+ "|2010-02-23| 199.999998| 201.330002| 195.709993| 197.059998|143773700| 25.531005|\n",
+ "|2014-06-09| 92.699997| 93.879997| 91.75| 93.699997| 75415000| 88.906324|\n",
+ "|2014-06-10| 94.730003| 95.050003| 93.57| 94.25| 62777000| 89.428189|\n",
+ "|2014-06-11| 94.129997| 94.760002| 93.470001| 93.860001| 45681000| 89.058142|\n",
+ "|2014-06-12| 94.040001| 94.120003| 91.900002| 92.290001| 54749000| 87.568463|\n",
+ "|2014-06-13| 92.199997| 92.440002| 90.879997| 91.279999| 54525000| 86.610132|\n",
+ "|2014-06-16| 91.510002| 92.75| 91.449997| 92.199997| 35561000| 87.483064|\n",
+ "|2014-06-17| 92.309998| 92.699997| 91.800003| 92.08000200000001| 29726000| 87.36920699999999|\n",
+ "|2014-06-18| 92.269997| 92.290001| 91.349998| 92.18| 33514000| 87.46409|\n",
+ "|2014-06-19| 92.290001| 92.300003| 91.339996| 91.860001| 35528000| 87.160461|\n",
+ "|2014-06-20| 91.849998| 92.550003| 90.900002| 90.910004|100898000| 86.259066|\n",
+ "+----------+------------------+------------------+------------------+------------------+---------+------------------+\n",
+ "only showing top 20 rows\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.filter((df['Close'] <200) & ~(df['Open']>200)).show()\n",
+ "# ~ for not"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+----------+------------------+----------+------+------+---------+---------+\n",
+ "| Date| Open| High| Low| Close| Volume|Adj Close|\n",
+ "+----------+------------------+----------+------+------+---------+---------+\n",
+ "|2010-01-22|206.78000600000001|207.499996|197.16|197.75|220441900|25.620401|\n",
+ "+----------+------------------+----------+------+------+---------+---------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.filter(df['Low'] ==197.16).show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[Row(Date='2010-01-22', Open=206.78000600000001, High=207.499996, Low=197.16, Close=197.75, Volume=220441900, Adj Close=25.620401)]"
+ ]
+ },
+ "execution_count": 54,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.filter(df['Low'] ==197.16).collect()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "result=df.filter(df['Low'] ==197.16).collect()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "row=result[0]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'Date': '2010-01-22',\n",
+ " 'Open': 206.78000600000001,\n",
+ " 'High': 207.499996,\n",
+ " 'Low': 197.16,\n",
+ " 'Close': 197.75,\n",
+ " 'Volume': 220441900,\n",
+ " 'Adj Close': 25.620401}"
+ ]
+ },
+ "execution_count": 59,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "row.asDict()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "220441900"
+ ]
+ },
+ "execution_count": 60,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "row.asDict()['Volume']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## next lesson"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "spark=SparkSession.builder.appName('aggs').getOrCreate()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df=spark.read.csv('sales_info.csv',inferSchema=True,header=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+-------+-------+-----+\n",
+ "|Company| Person|Sales|\n",
+ "+-------+-------+-----+\n",
+ "| GOOG| Sam|200.0|\n",
+ "| GOOG|Charlie|120.0|\n",
+ "| GOOG| Frank|340.0|\n",
+ "| MSFT| Tina|600.0|\n",
+ "| MSFT| Amy|124.0|\n",
+ "| MSFT|Vanessa|243.0|\n",
+ "| FB| Carl|870.0|\n",
+ "| FB| Sarah|350.0|\n",
+ "| APPL| John|250.0|\n",
+ "| APPL| Linda|130.0|\n",
+ "| APPL| Mike|750.0|\n",
+ "| APPL| Chris|350.0|\n",
+ "+-------+-------+-----+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "root\n",
+ " |-- Company: string (nullable = true)\n",
+ " |-- Person: string (nullable = true)\n",
+ " |-- Sales: double (nullable = true)\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.printSchema()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<pyspark.sql.group.GroupedData at 0x7f0762785250>"
+ ]
+ },
+ "execution_count": 65,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.groupBy(\"Company\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+-------+-----------------+\n",
+ "|Company| avg(Sales)|\n",
+ "+-------+-----------------+\n",
+ "| APPL| 370.0|\n",
+ "| GOOG| 220.0|\n",
+ "| FB| 610.0|\n",
+ "| MSFT|322.3333333333333|\n",
+ "+-------+-----------------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.groupBy(\"Company\").mean().show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+-------+----------+\n",
+ "|Company|sum(Sales)|\n",
+ "+-------+----------+\n",
+ "| APPL| 1480.0|\n",
+ "| GOOG| 660.0|\n",
+ "| FB| 1220.0|\n",
+ "| MSFT| 967.0|\n",
+ "+-------+----------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.groupBy(\"Company\").sum().show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+-------+----------+\n",
+ "|Company|max(Sales)|\n",
+ "+-------+----------+\n",
+ "| APPL| 750.0|\n",
+ "| GOOG| 340.0|\n",
+ "| FB| 870.0|\n",
+ "| MSFT| 600.0|\n",
+ "+-------+----------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.groupBy(\"Company\").max().show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+-------+-----+\n",
+ "|Company|count|\n",
+ "+-------+-----+\n",
+ "| APPL| 4|\n",
+ "| GOOG| 3|\n",
+ "| FB| 2|\n",
+ "| MSFT| 3|\n",
+ "+-------+-----+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.groupBy(\"Company\").count().show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+----------+\n",
+ "|sum(Sales)|\n",
+ "+----------+\n",
+ "| 4327.0|\n",
+ "+----------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.agg({'Sales':'sum'}).show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# df.agg({'column name':'function'}).show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "group_data=df.groupBy('Company')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+-------+----------+\n",
+ "|Company|max(Sales)|\n",
+ "+-------+----------+\n",
+ "| APPL| 750.0|\n",
+ "| GOOG| 340.0|\n",
+ "| FB| 870.0|\n",
+ "| MSFT| 600.0|\n",
+ "+-------+----------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "group_data.agg({'Sales':'max'}).show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 75,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# import functions form spark\n",
+ "from pyspark.sql.functions import countDistinct,avg,stddev"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+---------------------+\n",
+ "|count(DISTINCT Sales)|\n",
+ "+---------------------+\n",
+ "| 11|\n",
+ "+---------------------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.select(countDistinct('Sales')).show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+-----------------+\n",
+ "| avg(Sales)|\n",
+ "+-----------------+\n",
+ "|360.5833333333333|\n",
+ "+-----------------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.select(avg('Sales')).show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 78,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+-----------------+\n",
+ "| Average Sales|\n",
+ "+-----------------+\n",
+ "|360.5833333333333|\n",
+ "+-----------------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.select(avg('Sales').alias('Average Sales')).show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 79,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+------------------+\n",
+ "|stddev_samp(Sales)|\n",
+ "+------------------+\n",
+ "|250.08742410799007|\n",
+ "+------------------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.select(stddev('Sales')).show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pyspark.sql.functions import format_number"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 82,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sales_std =df.select(stddev('Sales').alias('std'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+---------------------+\n",
+ "|format_number(std, 2)|\n",
+ "+---------------------+\n",
+ "| 250.09|\n",
+ "+---------------------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "sales_std.select(format_number('std',2)).show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 85,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+------+\n",
+ "| STD|\n",
+ "+------+\n",
+ "|250.09|\n",
+ "+------+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "sales_std.select(format_number('std',2).alias('STD')).show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 86,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# sort by sales coumn"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 87,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+-------+-------+-----+\n",
+ "|Company| Person|Sales|\n",
+ "+-------+-------+-----+\n",
+ "| GOOG|Charlie|120.0|\n",
+ "| MSFT| Amy|124.0|\n",
+ "| APPL| Linda|130.0|\n",
+ "| GOOG| Sam|200.0|\n",
+ "| MSFT|Vanessa|243.0|\n",
+ "| APPL| John|250.0|\n",
+ "| GOOG| Frank|340.0|\n",
+ "| FB| Sarah|350.0|\n",
+ "| APPL| Chris|350.0|\n",
+ "| MSFT| Tina|600.0|\n",
+ "| APPL| Mike|750.0|\n",
+ "| FB| Carl|870.0|\n",
+ "+-------+-------+-----+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.orderBy(\"Sales\").show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 89,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+-------+-------+-----+\n",
+ "|Company| Person|Sales|\n",
+ "+-------+-------+-----+\n",
+ "| FB| Carl|870.0|\n",
+ "| APPL| Mike|750.0|\n",
+ "| MSFT| Tina|600.0|\n",
+ "| FB| Sarah|350.0|\n",
+ "| APPL| Chris|350.0|\n",
+ "| GOOG| Frank|340.0|\n",
+ "| APPL| John|250.0|\n",
+ "| MSFT|Vanessa|243.0|\n",
+ "| GOOG| Sam|200.0|\n",
+ "| APPL| Linda|130.0|\n",
+ "| MSFT| Amy|124.0|\n",
+ "| GOOG|Charlie|120.0|\n",
+ "+-------+-------+-----+\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.orderBy(df['Sales'].desc()).show()"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
diff --git a/appl_stock.csv b/appl_stock.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e4e4409d7b4abe1ca4dc9e2df24d39d410a9c86a
--- /dev/null
+++ b/appl_stock.csv
@@ -0,0 +1,1763 @@
+Date,Open,High,Low,Close,Volume,Adj Close
+2010-01-04,213.429998,214.499996,212.38000099999996,214.009998,123432400,27.727039
+2010-01-05,214.599998,215.589994,213.249994,214.379993,150476200,27.774976000000002
+2010-01-06,214.379993,215.23,210.750004,210.969995,138040000,27.333178000000004
+2010-01-07,211.75,212.000006,209.050005,210.58,119282800,27.28265
+2010-01-08,210.299994,212.000006,209.06000500000002,211.98000499999998,111902700,27.464034
+2010-01-11,212.79999700000002,213.000002,208.450005,210.11000299999998,115557400,27.221758
+2010-01-12,209.18999499999998,209.76999500000002,206.419998,207.720001,148614900,26.91211
+2010-01-13,207.870005,210.92999500000002,204.099998,210.650002,151473000,27.29172
+2010-01-14,210.11000299999998,210.45999700000002,209.020004,209.43,108223500,27.133657
+2010-01-15,210.92999500000002,211.59999700000003,205.869999,205.93,148516900,26.680197999999997
+2010-01-19,208.330002,215.18999900000003,207.240004,215.039995,182501900,27.860484999999997
+2010-01-20,214.910006,215.549994,209.500002,211.73,153038200,27.431644
+2010-01-21,212.079994,213.30999599999998,207.210003,208.069996,152038600,26.957455
+2010-01-22,206.78000600000001,207.499996,197.16,197.75,220441900,25.620401
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+2016-12-30,116.650002,117.199997,115.43,115.82,30586300,115.32002
diff --git a/sales_info.csv b/sales_info.csv
new file mode 100644
index 0000000000000000000000000000000000000000..a5f739bdd25839e7f552e2bb977b3215458cdef1
--- /dev/null
+++ b/sales_info.csv
@@ -0,0 +1,13 @@
+Company,Person,Sales
+GOOG,Sam,200
+GOOG,Charlie,120
+GOOG,Frank,340
+MSFT,Tina,600
+MSFT,Amy,124
+MSFT,Vanessa,243
+FB,Carl,870
+FB,Sarah,350
+APPL,John,250
+APPL,Linda, 130
+APPL,Mike, 750
+APPL, Chris, 350
\ No newline at end of file