Notice
Recent Posts
Recent Comments
Link
«   2025/04   »
일 월 화 수 목 금 토
1 2 3 4 5
6 7 8 9 10 11 12
13 14 15 16 17 18 19
20 21 22 23 24 25 26
27 28 29 30
Tags more
Archives
Today
Total
관리 메뉴

Hee'World

Spark DataFrame 02 (Pyspark) 본문

BigData/Spark

Spark DataFrame 02 (Pyspark)

Jonghee Jeon 2020. 4. 11. 16:17

 

 

 

Spark DataFrame

 

- select

In [3]:
df = spark.read.json("data/2015-summary.json")
In [6]:
df.printSchema()
 
root
 |-- DEST_COUNTRY_NAME: string (nullable = true)
 |-- ORIGIN_COUNTRY_NAME: string (nullable = true)
 |-- count: long (nullable = true)
In [8]:
df.select("DEST_COUNTRY_NAME").show()
 
+--------------------+
|   DEST_COUNTRY_NAME|
+--------------------+
|       United States|
|       United States|
|       United States|
|               Egypt|
|       United States|
|       United States|
|       United States|
|          Costa Rica|
|             Senegal|
|             Moldova|
|       United States|
|       United States|
|              Guyana|
|               Malta|
|            Anguilla|
|             Bolivia|
|       United States|
|             Algeria|
|Turks and Caicos ...|
|       United States|
+--------------------+
only showing top 20 rows
In [9]:
df.select(["DEST_COUNTRY_NAME","count"]).show()
 
+--------------------+-----+
|   DEST_COUNTRY_NAME|count|
+--------------------+-----+
|       United States|   15|
|       United States|    1|
|       United States|  344|
|               Egypt|   15|
|       United States|   62|
|       United States|    1|
|       United States|   62|
|          Costa Rica|  588|
|             Senegal|   40|
|             Moldova|    1|
|       United States|  325|
|       United States|   39|
|              Guyana|   64|
|               Malta|    1|
|            Anguilla|   41|
|             Bolivia|   30|
|       United States|    6|
|             Algeria|    4|
|Turks and Caicos ...|  230|
|       United States|    1|
+--------------------+-----+
only showing top 20 rows
 

withColumn

In [12]:
df.withColumn("newCount", df["count"]+2).show()
 
+--------------------+-------------------+-----+--------+
|   DEST_COUNTRY_NAME|ORIGIN_COUNTRY_NAME|count|newCount|
+--------------------+-------------------+-----+--------+
|       United States|            Romania|   15|      17|
|       United States|            Croatia|    1|       3|
|       United States|            Ireland|  344|     346|
|               Egypt|      United States|   15|      17|
|       United States|              India|   62|      64|
|       United States|          Singapore|    1|       3|
|       United States|            Grenada|   62|      64|
|          Costa Rica|      United States|  588|     590|
|             Senegal|      United States|   40|      42|
|             Moldova|      United States|    1|       3|
|       United States|       Sint Maarten|  325|     327|
|       United States|   Marshall Islands|   39|      41|
|              Guyana|      United States|   64|      66|
|               Malta|      United States|    1|       3|
|            Anguilla|      United States|   41|      43|
|             Bolivia|      United States|   30|      32|
|       United States|           Paraguay|    6|       8|
|             Algeria|      United States|    4|       6|
|Turks and Caicos ...|      United States|  230|     232|
|       United States|          Gibraltar|    1|       3|
+--------------------+-------------------+-----+--------+
only showing top 20 rows
In [15]:
df.withColumnRenamed("count", "renameCount").show()
 
+--------------------+-------------------+-----------+
|   DEST_COUNTRY_NAME|ORIGIN_COUNTRY_NAME|renameCount|
+--------------------+-------------------+-----------+
|       United States|            Romania|         15|
|       United States|            Croatia|          1|
|       United States|            Ireland|        344|
|               Egypt|      United States|         15|
|       United States|              India|         62|
|       United States|          Singapore|          1|
|       United States|            Grenada|         62|
|          Costa Rica|      United States|        588|
|             Senegal|      United States|         40|
|             Moldova|      United States|          1|
|       United States|       Sint Maarten|        325|
|       United States|   Marshall Islands|         39|
|              Guyana|      United States|         64|
|               Malta|      United States|          1|
|            Anguilla|      United States|         41|
|             Bolivia|      United States|         30|
|       United States|           Paraguay|          6|
|             Algeria|      United States|          4|
|Turks and Caicos ...|      United States|        230|
|       United States|          Gibraltar|          1|
+--------------------+-------------------+-----------+
only showing top 20 rows
 

groupby

In [18]:
df.groupBy("DEST_COUNTRY_NAME").count().show()
 
+--------------------+-----+
|   DEST_COUNTRY_NAME|count|
+--------------------+-----+
|            Anguilla|    1|
|              Russia|    1|
|            Paraguay|    1|
|             Senegal|    1|
|              Sweden|    1|
|            Kiribati|    1|
|              Guyana|    1|
|         Philippines|    1|
|            Djibouti|    1|
|            Malaysia|    1|
|           Singapore|    1|
|                Fiji|    1|
|              Turkey|    1|
|                Iraq|    1|
|             Germany|    1|
|              Jordan|    1|
|               Palau|    1|
|Turks and Caicos ...|    1|
|              France|    1|
|              Greece|    1|
+--------------------+-----+
only showing top 20 rows
 

filter

In [19]:
df = spark.read.csv("data/appl_stock.csv", inferSchema=True, header=True)
In [20]:
df.printSchema()
 
root
 |-- Date: timestamp (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]:
df.filter("Close < 500").show()
 
+-------------------+------------------+------------------+------------------+------------------+---------+------------------+
|               Date|              Open|              High|               Low|             Close|   Volume|         Adj Close|
+-------------------+------------------+------------------+------------------+------------------+---------+------------------+
|2010-01-04 00:00:00|        213.429998|        214.499996|212.38000099999996|        214.009998|123432400|         27.727039|
|2010-01-05 00:00:00|        214.599998|        215.589994|        213.249994|        214.379993|150476200|27.774976000000002|
|2010-01-06 00:00:00|        214.379993|            215.23|        210.750004|        210.969995|138040000|27.333178000000004|
|2010-01-07 00:00:00|            211.75|        212.000006|        209.050005|            210.58|119282800|          27.28265|
|2010-01-08 00:00:00|        210.299994|        212.000006|209.06000500000002|211.98000499999998|111902700|         27.464034|
|2010-01-11 00:00:00|212.79999700000002|        213.000002|        208.450005|210.11000299999998|115557400|         27.221758|
|2010-01-12 00:00:00|209.18999499999998|209.76999500000002|        206.419998|        207.720001|148614900|          26.91211|
|2010-01-13 00:00:00|        207.870005|210.92999500000002|        204.099998|        210.650002|151473000|          27.29172|
|2010-01-14 00:00:00|210.11000299999998|210.45999700000002|        209.020004|            209.43|108223500|         27.133657|
|2010-01-15 00:00:00|210.92999500000002|211.59999700000003|        205.869999|            205.93|148516900|26.680197999999997|
|2010-01-19 00:00:00|        208.330002|215.18999900000003|        207.240004|        215.039995|182501900|27.860484999999997|
|2010-01-20 00:00:00|        214.910006|        215.549994|        209.500002|            211.73|153038200|         27.431644|
|2010-01-21 00:00:00|        212.079994|213.30999599999998|        207.210003|        208.069996|152038600|         26.957455|
|2010-01-22 00:00:00|206.78000600000001|        207.499996|            197.16|            197.75|220441900|         25.620401|
|2010-01-25 00:00:00|202.51000200000001|        204.699999|        200.190002|        203.070002|266424900|26.309658000000002|
|2010-01-26 00:00:00|205.95000100000001|        213.710005|        202.580004|        205.940001|466777500|         26.681494|
|2010-01-27 00:00:00|        206.849995|            210.58|        199.530001|        207.880005|430642100|26.932840000000002|
|2010-01-28 00:00:00|        204.930004|        205.500004|        198.699995|        199.289995|293375600|25.819922000000002|
|2010-01-29 00:00:00|        201.079996|        202.199995|        190.250002|        192.060003|311488100|         24.883208|
|2010-02-01 00:00:00|192.36999699999998|             196.0|191.29999899999999|        194.729998|187469100|         25.229131|
+-------------------+------------------+------------------+------------------+------------------+---------+------------------+
only showing top 20 rows
In [23]:
df.filter("Close < 500").select("Open").show()
 
+------------------+
|              Open|
+------------------+
|        213.429998|
|        214.599998|
|        214.379993|
|            211.75|
|        210.299994|
|212.79999700000002|
|209.18999499999998|
|        207.870005|
|210.11000299999998|
|210.92999500000002|
|        208.330002|
|        214.910006|
|        212.079994|
|206.78000600000001|
|202.51000200000001|
|205.95000100000001|
|        206.849995|
|        204.930004|
|        201.079996|
|192.36999699999998|
+------------------+
only showing top 20 rows
In [24]:
df.filter("Close < 500").select(["Open", "Close"]).show()
 
+------------------+------------------+
|              Open|             Close|
+------------------+------------------+
|        213.429998|        214.009998|
|        214.599998|        214.379993|
|        214.379993|        210.969995|
|            211.75|            210.58|
|        210.299994|211.98000499999998|
|212.79999700000002|210.11000299999998|
|209.18999499999998|        207.720001|
|        207.870005|        210.650002|
|210.11000299999998|            209.43|
|210.92999500000002|            205.93|
|        208.330002|        215.039995|
|        214.910006|            211.73|
|        212.079994|        208.069996|
|206.78000600000001|            197.75|
|202.51000200000001|        203.070002|
|205.95000100000001|        205.940001|
|        206.849995|        207.880005|
|        204.930004|        199.289995|
|        201.079996|        192.060003|
|192.36999699999998|        194.729998|
+------------------+------------------+
only showing top 20 rows
In [25]:
df.filter(df["Close"] < 200).show()
 
+-------------------+------------------+------------------+------------------+------------------+---------+------------------+
|               Date|              Open|              High|               Low|             Close|   Volume|         Adj Close|
+-------------------+------------------+------------------+------------------+------------------+---------+------------------+
|2010-01-22 00:00:00|206.78000600000001|        207.499996|            197.16|            197.75|220441900|         25.620401|
|2010-01-28 00:00:00|        204.930004|        205.500004|        198.699995|        199.289995|293375600|25.819922000000002|
|2010-01-29 00:00:00|        201.079996|        202.199995|        190.250002|        192.060003|311488100|         24.883208|
|2010-02-01 00:00:00|192.36999699999998|             196.0|191.29999899999999|        194.729998|187469100|         25.229131|
|2010-02-02 00:00:00|        195.909998|        196.319994|193.37999299999998|        195.859997|174585600|25.375532999999997|
|2010-02-03 00:00:00|        195.169994|        200.200003|        194.420004|        199.229994|153832000|25.812148999999998|
|2010-02-04 00:00:00|        196.730003|        198.370001|        191.570005|        192.050003|189413000|         24.881912|
|2010-02-05 00:00:00|192.63000300000002|             196.0|        190.850002|        195.460001|212576700|25.323710000000002|
|2010-02-08 00:00:00|        195.690006|197.88000300000002|        193.999994|194.11999699999998|119567700|           25.1501|
|2010-02-09 00:00:00|        196.419996|        197.499994|        194.749998|196.19000400000002|158221700|         25.418289|
|2010-02-10 00:00:00|        195.889997|             196.6|            194.26|195.12000700000002| 92590400|          25.27966|
|2010-02-11 00:00:00|        194.880001|        199.750006|194.05999599999998|        198.669994|137586400|         25.739595|
|2010-02-23 00:00:00|        199.999998|        201.330002|        195.709993|        197.059998|143773700|         25.531005|
|2014-06-09 00:00:00|         92.699997|         93.879997|             91.75|         93.699997| 75415000|         88.906324|
|2014-06-10 00:00:00|         94.730003|         95.050003|             93.57|             94.25| 62777000|         89.428189|
|2014-06-11 00:00:00|         94.129997|         94.760002|         93.470001|         93.860001| 45681000|         89.058142|
|2014-06-12 00:00:00|         94.040001|         94.120003|         91.900002|         92.290001| 54749000|         87.568463|
|2014-06-13 00:00:00|         92.199997|         92.440002|         90.879997|         91.279999| 54525000|         86.610132|
|2014-06-16 00:00:00|         91.510002|             92.75|         91.449997|         92.199997| 35561000|         87.483064|
|2014-06-17 00:00:00|         92.309998|         92.699997|         91.800003| 92.08000200000001| 29726000| 87.36920699999999|
+-------------------+------------------+------------------+------------------+------------------+---------+------------------+
only showing top 20 rows
In [28]:
df.filter((df["Close"] < 200) & (df['Open'] > 200)).show()
 
+-------------------+------------------+----------+----------+----------+---------+------------------+
|               Date|              Open|      High|       Low|     Close|   Volume|         Adj Close|
+-------------------+------------------+----------+----------+----------+---------+------------------+
|2010-01-22 00:00:00|206.78000600000001|207.499996|    197.16|    197.75|220441900|         25.620401|
|2010-01-28 00:00:00|        204.930004|205.500004|198.699995|199.289995|293375600|25.819922000000002|
|2010-01-29 00:00:00|        201.079996|202.199995|190.250002|192.060003|311488100|         24.883208|
+-------------------+------------------+----------+----------+----------+---------+------------------+
In [29]:
df.filter((df["Close"] < 200) | (df['Open'] > 200)).show()
 
+-------------------+------------------+------------------+------------------+------------------+---------+------------------+
|               Date|              Open|              High|               Low|             Close|   Volume|         Adj Close|
+-------------------+------------------+------------------+------------------+------------------+---------+------------------+
|2010-01-04 00:00:00|        213.429998|        214.499996|212.38000099999996|        214.009998|123432400|         27.727039|
|2010-01-05 00:00:00|        214.599998|        215.589994|        213.249994|        214.379993|150476200|27.774976000000002|
|2010-01-06 00:00:00|        214.379993|            215.23|        210.750004|        210.969995|138040000|27.333178000000004|
|2010-01-07 00:00:00|            211.75|        212.000006|        209.050005|            210.58|119282800|          27.28265|
|2010-01-08 00:00:00|        210.299994|        212.000006|209.06000500000002|211.98000499999998|111902700|         27.464034|
|2010-01-11 00:00:00|212.79999700000002|        213.000002|        208.450005|210.11000299999998|115557400|         27.221758|
|2010-01-12 00:00:00|209.18999499999998|209.76999500000002|        206.419998|        207.720001|148614900|          26.91211|
|2010-01-13 00:00:00|        207.870005|210.92999500000002|        204.099998|        210.650002|151473000|          27.29172|
|2010-01-14 00:00:00|210.11000299999998|210.45999700000002|        209.020004|            209.43|108223500|         27.133657|
|2010-01-15 00:00:00|210.92999500000002|211.59999700000003|        205.869999|            205.93|148516900|26.680197999999997|
|2010-01-19 00:00:00|        208.330002|215.18999900000003|        207.240004|        215.039995|182501900|27.860484999999997|
|2010-01-20 00:00:00|        214.910006|        215.549994|        209.500002|            211.73|153038200|         27.431644|
|2010-01-21 00:00:00|        212.079994|213.30999599999998|        207.210003|        208.069996|152038600|         26.957455|
|2010-01-22 00:00:00|206.78000600000001|        207.499996|            197.16|            197.75|220441900|         25.620401|
|2010-01-25 00:00:00|202.51000200000001|        204.699999|        200.190002|        203.070002|266424900|26.309658000000002|
|2010-01-26 00:00:00|205.95000100000001|        213.710005|        202.580004|        205.940001|466777500|         26.681494|
|2010-01-27 00:00:00|        206.849995|            210.58|        199.530001|        207.880005|430642100|26.932840000000002|
|2010-01-28 00:00:00|        204.930004|        205.500004|        198.699995|        199.289995|293375600|25.819922000000002|
|2010-01-29 00:00:00|        201.079996|        202.199995|        190.250002|        192.060003|311488100|         24.883208|
|2010-02-01 00:00:00|192.36999699999998|             196.0|191.29999899999999|        194.729998|187469100|         25.229131|
+-------------------+------------------+------------------+------------------+------------------+---------+------------------+
only showing top 20 rows
In [30]:
df.filter((df["Close"] < 200) & ~(df['Open'] > 200)).show()
 
+-------------------+------------------+------------------+------------------+------------------+---------+------------------+
|               Date|              Open|              High|               Low|             Close|   Volume|         Adj Close|
+-------------------+------------------+------------------+------------------+------------------+---------+------------------+
|2010-02-01 00:00:00|192.36999699999998|             196.0|191.29999899999999|        194.729998|187469100|         25.229131|
|2010-02-02 00:00:00|        195.909998|        196.319994|193.37999299999998|        195.859997|174585600|25.375532999999997|
|2010-02-03 00:00:00|        195.169994|        200.200003|        194.420004|        199.229994|153832000|25.812148999999998|
|2010-02-04 00:00:00|        196.730003|        198.370001|        191.570005|        192.050003|189413000|         24.881912|
|2010-02-05 00:00:00|192.63000300000002|             196.0|        190.850002|        195.460001|212576700|25.323710000000002|
|2010-02-08 00:00:00|        195.690006|197.88000300000002|        193.999994|194.11999699999998|119567700|           25.1501|
|2010-02-09 00:00:00|        196.419996|        197.499994|        194.749998|196.19000400000002|158221700|         25.418289|
|2010-02-10 00:00:00|        195.889997|             196.6|            194.26|195.12000700000002| 92590400|          25.27966|
|2010-02-11 00:00:00|        194.880001|        199.750006|194.05999599999998|        198.669994|137586400|         25.739595|
|2010-02-23 00:00:00|        199.999998|        201.330002|        195.709993|        197.059998|143773700|         25.531005|
|2014-06-09 00:00:00|         92.699997|         93.879997|             91.75|         93.699997| 75415000|         88.906324|
|2014-06-10 00:00:00|         94.730003|         95.050003|             93.57|             94.25| 62777000|         89.428189|
|2014-06-11 00:00:00|         94.129997|         94.760002|         93.470001|         93.860001| 45681000|         89.058142|
|2014-06-12 00:00:00|         94.040001|         94.120003|         91.900002|         92.290001| 54749000|         87.568463|
|2014-06-13 00:00:00|         92.199997|         92.440002|         90.879997|         91.279999| 54525000|         86.610132|
|2014-06-16 00:00:00|         91.510002|             92.75|         91.449997|         92.199997| 35561000|         87.483064|
|2014-06-17 00:00:00|         92.309998|         92.699997|         91.800003| 92.08000200000001| 29726000| 87.36920699999999|
|2014-06-18 00:00:00|         92.269997|         92.290001|         91.349998|             92.18| 33514000|          87.46409|
|2014-06-19 00:00:00|         92.290001|         92.300003|         91.339996|         91.860001| 35528000|         87.160461|
|2014-06-20 00:00:00|         91.849998|         92.550003|         90.900002|         90.910004|100898000|         86.259066|
+-------------------+------------------+------------------+------------------+------------------+---------+------------------+
only showing top 20 rows
In [32]:
df.filter(df["Low"] == 197.16).show()
 
+-------------------+------------------+----------+------+------+---------+---------+
|               Date|              Open|      High|   Low| Close|   Volume|Adj Close|
+-------------------+------------------+----------+------+------+---------+---------+
|2010-01-22 00:00:00|206.78000600000001|207.499996|197.16|197.75|220441900|25.620401|
+-------------------+------------------+----------+------+------+---------+---------+
In [ ]:
In [ ]:

'BigData > Spark' 카테고리의 다른 글

Spark SQL (PySpark)  (0) 2020.04.15
Spark DataFrame 03 (Pyspark)  (0) 2020.04.11
Spark DataFrame01 (Pyspark)  (0) 2020.04.11
Spark RDD 문법  (0) 2020.04.06
Spark RDD  (0) 2020.04.04
'BigData/Spark' Related Articles more
Comments