9. pd.DataFrame vs PySpark DataFrame¶
9.1. Create DataFrame¶
9.1.1. From List¶
my_list = [['a', 1, 2], ['b', 2, 3],['c', 3, 4]]
col_name = ['A', 'B', 'C']
:: Python Code:
# caution for the columns=
pd.DataFrame(my_list,columns= col_name)
#
spark.createDataFrame(my_list, col_name).show()
:: Comparison:
+---+---+---+
| A| B| C|
A B C +---+---+---+
0 a 1 2 | a| 1| 2|
1 b 2 3 | b| 2| 3|
2 c 3 4 | c| 3| 4|
+---+---+---+
Attention
Pay attention to the parameter columns= in pd.DataFrame. Since the default value will make the list as rows.
:: Python Code:# caution for the columns= pd.DataFrame(my_list, columns= col_name) # pd.DataFrame(my_list, col_name)
:: Comparison:A B C 0 1 2 0 a 1 2 A a 1 2 1 b 2 3 B b 2 3 2 c 3 4 C c 3 4
9.1.2. From Dict¶
data = {'A': [0, 1, 0],
'B': [1, 0, 1],
'C': [1, 0, 0]}
:: Python Code:
pd.DataFrame(data)
# Tedious for PySpark
spark.createDataFrame(np.array(list(d.values())).T.tolist(),list(d.keys())).show()
:: Comparison:
+---+---+---+
| A| B| C|
A B C +---+---+---+
0 0 1 1 | 0| 1| 1|
1 1 0 0 | 1| 0| 0|
2 0 1 0 | 0| 1| 0|
+---+---+---+
9.2. Convert between pandas and pyspark DataFrame¶
9.2.1. From pandas to pyspark DataFrame¶
:: Example:
# pd.DataFrame pandas_df: DataFrame pandas
# rdd.DataFrame. spark_df: DataFrame spark
pandas_df = pd.read_csv('Advertising.csv')
spark_df = spark.createDataFrame(pandas_df)
spark_df.printSchema()
9.2.2. From pyspark to pandas DataFrame¶
:: Example:
pandas_df = spark_df.toPandas()
pandas_df.info()
9.3. Load DataFrame¶
9.3.1. From DataBase¶
Most of time, you need to share your code with your colleagues or release your code for Code Review or Quality assurance(QA). You will definitely do not want to have your User Information in the code. So you can save them
in login.txt:
runawayhorse001
PythonTips
and use the following code to import your User Information:
#User Information
try:
login = pd.read_csv(r'login.txt', header=None)
user = login[0][0]
pw = login[0][1]
print('User information is ready!')
except:
print('Login information is not available!!!')
#Database information
host = '##.###.###.##'
db_name = 'db_name'
table_name = 'table_name'
:: Comparison:
conn = psycopg2.connect(host=host, database=db_name, user=user, password=pw)
cur = conn.cursor()
sql = """
select *
from {table_name}
""".format(table_name=table_name)
pandas_df = pd.read_sql(sql, conn)
# connect to database
url = 'jdbc:postgresql://'+host+':5432/'+db_name+'?user='+user+'&password='+pw
properties ={'driver': 'org.postgresql.Driver', 'password': pw,'user': user}
spark_df = spark.read.jdbc(url=url, table=table_name, properties=properties)
Attention
Reading tables from Database with PySpark neespark_df the proper drive for the corresponding Database. For example, the above demo neespark_df org.postgresql.Driver and you need to download it and put it in jars folder of your spark installation path. I download postgresql-42.1.1.jar from the official website and put it in jars folder.
9.3.2. From .csv¶
:: Comparison:
# pd.DataFrame pandas_df: DataFrame pandas
pandas_df = pd.read_csv('Advertising.csv')
#rdd.DataFrame. spark_df: DataFrame spark
spark_df = spark.read.csv(path='Advertising.csv',
# sep=',',
# encoding='UTF-8',
# comment=None,
header=True,
inferSchema=True)
9.3.3. From .json¶
Data from: http://api.luftdaten.info/static/v1/data.json
pandas_df = pd.read_json("data/data.json")
spark_df = spark.read.json('data/data.json')
:: Python Code:
pandas_df[['id','timestamp']].head(4)
#
spark_df[['id','timestamp']].show(4)
:: Comparison:
+----------+-------------------+
| id| timestamp|
id timestamp +----------+-------------------+
0 2994551481 2019-02-28 17:23:52 |2994551481|2019-02-28 17:23:52|
1 2994551482 2019-02-28 17:23:52 |2994551482|2019-02-28 17:23:52|
2 2994551483 2019-02-28 17:23:52 |2994551483|2019-02-28 17:23:52|
3 2994551484 2019-02-28 17:23:52 |2994551484|2019-02-28 17:23:52|
+----------+-------------------+
only showing top 4 rows
9.4. First n Rows¶
:: Python Code:
pandas_df.head(4)
#
spark_df.show(4)
:: Comparison:
+-----+-----+---------+-----+
| TV|Radio|Newspaper|Sales|
TV Radio Newspaper Sales +-----+-----+---------+-----+
0 230.1 37.8 69.2 22.1 |230.1| 37.8| 69.2| 22.1|
1 44.5 39.3 45.1 10.4 | 44.5| 39.3| 45.1| 10.4|
2 17.2 45.9 69.3 9.3 | 17.2| 45.9| 69.3| 9.3|
3 151.5 41.3 58.5 18.5 |151.5| 41.3| 58.5| 18.5|
+-----+-----+---------+-----+
only showing top 4 rows
9.5. Column Names¶
:: Python Code:
pandas_df.columns
#
spark_df.columns
:: Comparison:
Index(['TV', 'Radio', 'Newspaper', 'Sales'], dtype='object')
['TV', 'Radio', 'Newspaper', 'Sales']
9.6. Data types¶
:: Python Code:
pandas_df.dtypes
#
spark_df.dtypes
:: Comparison:
TV float64 [('TV', 'double'),
Radio float64 ('Radio', 'double'),
Newspaper float64 ('Newspaper', 'double'),
Sales float64 ('Sales', 'double')]
dtype: object
9.7. Replace Data types¶
my_list = [('a', 2, 3),
('b', 5, 6),
('c', 8, 9),
('a', 2, 3),
('b', 5, 6),
('c', 8, 9)]
col_name = ['col1', 'col2', 'col3']
pandas_df = pd.DataFrame(my_list,columns=col_name)
spark_df = spark.createDataFrame(pandas_df)
pandas_df.dtypes
col1 object
col2 int64
col3 int64
dtype: object
:: Python Code:
d = {'col2': 'string','col3':'string'}
pandas_df = pandas_df.astype({'col2': 'str','col3':'str'})
spark_df = spark_df.select(*list(set(spark_df.columns)-set(d.keys())),
*(col(c[0]).astype(c[1]).alias(c[0]) for c in d.items()))
:: Comparison:
col1 object
col2 object [('col1', 'string'), ('col2', 'string'), ('col3', 'string')]
col3 object
dtype: object
9.8. Fill Null¶
my_list = [['a', 1, None], ['b', 2, 3],['c', 3, 4]]
pandas_df = pd.DataFrame(my_list,columns=['A', 'B', 'C'])
spark_df = spark.createDataFrame(my_list, ['A', 'B', 'C'])
#
pandas_df.head()
spark_df.show()
:: Comparison:
+------+---+----+
| A| B| C|
A B C +------+---+----+
0 male 1 NaN | male| 1|null|
1 female 2 3.0 |female| 2| 3|
2 male 3 4.0 | male| 3| 4|
+------+---+----+
:: Python Code:
pandas_df.fillna(-99)
#
spark_df.fillna(-99).show()
:: Comparison:
+------+---+----+
| A| B| C|
A B C +------+---+----+
0 male 1 -99 | male| 1| -99|
1 female 2 3.0 |female| 2| 3|
2 male 3 4.0 | male| 3| 4|
+------+---+----+
9.9. Replace Values¶
:: Python Code:
# caution: you need to chose specific col
pandas_df.A.replace(['male', 'female'],[1, 0], inplace=True)
pandas_df
#caution: Mixed type replacements are not supported
spark_df.na.replace(['male','female'],['1','0']).show()
:: Comparison:
+---+---+----+
| A| B| C|
A B C +---+---+----+
0 1 1 NaN | 1| 1|null|
1 0 2 3.0 | 0| 2| 3|
2 1 3 4.0 | 1| 3| 4|
+---+---+----+
9.10. Rename Columns¶
9.10.1. Rename all columns¶
:: Python Code:
pandas_df.columns = ['a','b','c','d']
pandas_df.head(4)
#
spark_df.toDF('a','b','c','d').show(4)
:: Comparison:
+-----+----+----+----+
| a| b| c| d|
a b c d +-----+----+----+----+
0 230.1 37.8 69.2 22.1 |230.1|37.8|69.2|22.1|
1 44.5 39.3 45.1 10.4 | 44.5|39.3|45.1|10.4|
2 17.2 45.9 69.3 9.3 | 17.2|45.9|69.3| 9.3|
3 151.5 41.3 58.5 18.5 |151.5|41.3|58.5|18.5|
+-----+----+----+----+
only showing top 4 rows
9.10.2. Rename one or more columns¶
mapping = {'Newspaper':'C','Sales':'D'}
:: Python Code:
pandas_df.rename(columns=mapping).head(4)
#
new_names = [mapping.get(col,col) for col in spark_df.columns]
spark_df.toDF(*new_names).show(4)
:: Comparison:
+-----+-----+----+----+
| TV|Radio| C| D|
TV Radio C D +-----+-----+----+----+
0 230.1 37.8 69.2 22.1 |230.1| 37.8|69.2|22.1|
1 44.5 39.3 45.1 10.4 | 44.5| 39.3|45.1|10.4|
2 17.2 45.9 69.3 9.3 | 17.2| 45.9|69.3| 9.3|
3 151.5 41.3 58.5 18.5 |151.5| 41.3|58.5|18.5|
+-----+-----+----+----+
only showing top 4 rows
Note
You can also use withColumnRenamed to rename one column in PySpark.
:: Python Code:
spark_df.withColumnRenamed('Newspaper','Paper').show(4
:: Comparison:
+-----+-----+-----+-----+
| TV|Radio|Paper|Sales|
+-----+-----+-----+-----+
|230.1| 37.8| 69.2| 22.1|
| 44.5| 39.3| 45.1| 10.4|
| 17.2| 45.9| 69.3| 9.3|
|151.5| 41.3| 58.5| 18.5|
+-----+-----+-----+-----+
only showing top 4 rows
9.11. Drop Columns¶
drop_name = ['Newspaper','Sales']
:: Python Code:
pandas_df.drop(drop_name,axis=1).head(4)
#
spark_df.drop(*drop_name).show(4)
:: Comparison:
+-----+-----+
| TV|Radio|
TV Radio +-----+-----+
0 230.1 37.8 |230.1| 37.8|
1 44.5 39.3 | 44.5| 39.3|
2 17.2 45.9 | 17.2| 45.9|
3 151.5 41.3 |151.5| 41.3|
+-----+-----+
only showing top 4 rows
9.12. Filter¶
pandas_df = pd.read_csv('Advertising.csv')
#
spark_df = spark.read.csv(path='Advertising.csv',
header=True,
inferSchema=True)
:: Python Code:
pandas_df[pandas_df.Newspaper<20].head(4)
#
spark_df[spark_df.Newspaper<20].show(4)
:: Comparison:
+-----+-----+---------+-----+
| TV|Radio|Newspaper|Sales|
TV Radio Newspaper Sales +-----+-----+---------+-----+
7 120.2 19.6 11.6 13.2 |120.2| 19.6| 11.6| 13.2|
8 8.6 2.1 1.0 4.8 | 8.6| 2.1| 1.0| 4.8|
11 214.7 24.0 4.0 17.4 |214.7| 24.0| 4.0| 17.4|
13 97.5 7.6 7.2 9.7 | 97.5| 7.6| 7.2| 9.7|
+-----+-----+---------+-----+
only showing top 4 rows
:: Python Code:
pandas_df[(pandas_df.Newspaper<20)&(pandas_df.TV>100)].head(4)
#
spark_df[(spark_df.Newspaper<20)&(spark_df.TV>100)].show(4)
:: Comparison:
+-----+-----+---------+-----+
| TV|Radio|Newspaper|Sales|
TV Radio Newspaper Sales +-----+-----+---------+-----+
7 120.2 19.6 11.6 13.2 |120.2| 19.6| 11.6| 13.2|
11 214.7 24.0 4.0 17.4 |214.7| 24.0| 4.0| 17.4|
19 147.3 23.9 19.1 14.6 |147.3| 23.9| 19.1| 14.6|
25 262.9 3.5 19.5 12.0 |262.9| 3.5| 19.5| 12.0|
+-----+-----+---------+-----+
only showing top 4 rows
9.13. With New Column¶
:: Python Code:
pandas_df['tv_norm'] = pandas_df.TV/sum(pandas_df.TV)
pandas_df.head(4)
#
spark_df.withColumn('tv_norm', spark_df.TV/spark_df.groupBy().agg(F.sum("TV")).collect()[0][0]).show(4)
:: Comparison:
+-----+-----+---------+-----+--------------------+
| TV|Radio|Newspaper|Sales| tv_norm|
TV Radio Newspaper Sales tv_norm +-----+-----+---------+-----+--------------------+
0 230.1 37.8 69.2 22.1 0.007824 |230.1| 37.8| 69.2| 22.1|0.007824268493802813|
1 44.5 39.3 45.1 10.4 0.001513 | 44.5| 39.3| 45.1| 10.4|0.001513167961643...|
2 17.2 45.9 69.3 9.3 0.000585 | 17.2| 45.9| 69.3| 9.3|5.848649200061207E-4|
3 151.5 41.3 58.5 18.5 0.005152 |151.5| 41.3| 58.5| 18.5|0.005151571824472517|
+-----+-----+---------+-----+--------------------+
only showing top 4 rows
:: Python Code:
pandas_df['cond'] = pandas_df.apply(lambda c: 1 if ((c.TV>100)&(c.Radio<40)) else 2 if c.Sales> 10 else 3,axis=1)
#
spark_df.withColumn('cond',F.when((spark_df.TV>100)&(spark_df.Radio<40),1)\
.when(spark_df.Sales>10, 2)\
.otherwise(3)).show(4)
:: Comparison:
+-----+-----+---------+-----+----+
| TV|Radio|Newspaper|Sales|cond|
TV Radio Newspaper Sales cond +-----+-----+---------+-----+----+
0 230.1 37.8 69.2 22.1 1 |230.1| 37.8| 69.2| 22.1| 1|
1 44.5 39.3 45.1 10.4 2 | 44.5| 39.3| 45.1| 10.4| 2|
2 17.2 45.9 69.3 9.3 3 | 17.2| 45.9| 69.3| 9.3| 3|
3 151.5 41.3 58.5 18.5 2 |151.5| 41.3| 58.5| 18.5| 2|
+-----+-----+---------+-----+----+
only showing top 4 rows
:: Python Code:
pandas_df['log_tv'] = np.log(pandas_df.TV)
pandas_df.head(4)
#
spark_df.withColumn('log_tv',F.log(spark_df.TV)).show(4)
:: Comparison:
+-----+-----+---------+-----+------------------+
| TV|Radio|Newspaper|Sales| log_tv|
TV Radio Newspaper Sales log_tv +-----+-----+---------+-----+------------------+
0 230.1 37.8 69.2 22.1 5.438514 |230.1| 37.8| 69.2| 22.1| 5.43851399704132|
1 44.5 39.3 45.1 10.4 3.795489 | 44.5| 39.3| 45.1| 10.4|3.7954891891721947|
2 17.2 45.9 69.3 9.3 2.844909 | 17.2| 45.9| 69.3| 9.3|2.8449093838194073|
3 151.5 41.3 58.5 18.5 5.020586 |151.5| 41.3| 58.5| 18.5| 5.020585624949423|
+-----+-----+---------+-----+------------------+
only showing top 4 rows
:: Python Code:
pandas_df['tv+10'] = pandas_df.TV.apply(lambda x: x+10)
pandas_df.head(4)
#
spark_df.withColumn('tv+10', spark_df.TV+10).show(4)
:: Comparison:
+-----+-----+---------+-----+-----+
| TV|Radio|Newspaper|Sales|tv+10|
TV Radio Newspaper Sales tv+10 +-----+-----+---------+-----+-----+
0 230.1 37.8 69.2 22.1 240.1 |230.1| 37.8| 69.2| 22.1|240.1|
1 44.5 39.3 45.1 10.4 54.5 | 44.5| 39.3| 45.1| 10.4| 54.5|
2 17.2 45.9 69.3 9.3 27.2 | 17.2| 45.9| 69.3| 9.3| 27.2|
3 151.5 41.3 58.5 18.5 161.5 |151.5| 41.3| 58.5| 18.5|161.5|
+-----+-----+---------+-----+-----+
only showing top 4 rows
9.14. Join¶
leftp = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['B0', 'B1', 'B2', 'B3'],
'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']},
index=[0, 1, 2, 3])
rightp = pd.DataFrame({'A': ['A0', 'A1', 'A6', 'A7'],
'F': ['B4', 'B5', 'B6', 'B7'],
'G': ['C4', 'C5', 'C6', 'C7'],
'H': ['D4', 'D5', 'D6', 'D7']},
index=[4, 5, 6, 7])
lefts = spark.createDataFrame(leftp)
rights = spark.createDataFrame(rightp)
A B C D A F G H
0 A0 B0 C0 D0 4 A0 B4 C4 D4
1 A1 B1 C1 D1 5 A1 B5 C5 D5
2 A2 B2 C2 D2 6 A6 B6 C6 D6
3 A3 B3 C3 D3 7 A7 B7 C7 D7
9.14.1. Left Join¶
:: Python Code:
leftp.merge(rightp,on='A',how='left')
#
lefts.join(rights,on='A',how='left')
.orderBy('A',ascending=True).show()
:: Comparison:
+---+---+---+---+----+----+----+
| A| B| C| D| F| G| H|
A B C D F G H +---+---+---+---+----+----+----+
0 A0 B0 C0 D0 B4 C4 D4 | A0| B0| C0| D0| B4| C4| D4|
1 A1 B1 C1 D1 B5 C5 D5 | A1| B1| C1| D1| B5| C5| D5|
2 A2 B2 C2 D2 NaN NaN NaN | A2| B2| C2| D2|null|null|null|
3 A3 B3 C3 D3 NaN NaN NaN | A3| B3| C3| D3|null|null|null|
+---+---+---+---+----+----+----+
9.14.2. Right Join¶
:: Python Code:
leftp.merge(rightp,on='A',how='right')
#
lefts.join(rights,on='A',how='right')
.orderBy('A',ascending=True).show()
:: Comparison:
+---+----+----+----+---+---+---+
| A| B| C| D| F| G| H|
A B C D F G H +---+----+----+----+---+---+---+
0 A0 B0 C0 D0 B4 C4 D4 | A0| B0| C0| D0| B4| C4| D4|
1 A1 B1 C1 D1 B5 C5 D5 | A1| B1| C1| D1| B5| C5| D5|
2 A6 NaN NaN NaN B6 C6 D6 | A6|null|null|null| B6| C6| D6|
3 A7 NaN NaN NaN B7 C7 D7 | A7|null|null|null| B7| C7| D7|
+---+----+----+----+---+---+---+
9.14.3. Inner Join¶
:: Python Code:
leftp.merge(rightp,on='A',how='inner')
#
lefts.join(rights,on='A',how='inner')
.orderBy('A',ascending=True).show()
:: Comparison:
+---+---+---+---+---+---+---+
| A| B| C| D| F| G| H|
A B C D F G H +---+---+---+---+---+---+---+
0 A0 B0 C0 D0 B4 C4 D4 | A0| B0| C0| D0| B4| C4| D4|
1 A1 B1 C1 D1 B5 C5 D5 | A1| B1| C1| D1| B5| C5| D5|
+---+---+---+---+---+---+---+
9.14.4. Full Join¶
:: Python Code:
leftp.merge(rightp,on='A',how='full')
#
lefts.join(rights,on='A',how='full')
.orderBy('A',ascending=True).show()
:: Comparison:
+---+----+----+----+----+----+----+
| A| B| C| D| F| G| H|
A B C D F G H +---+----+----+----+----+----+----+
0 A0 B0 C0 D0 B4 C4 D4 | A0| B0| C0| D0| B4| C4| D4|
1 A1 B1 C1 D1 B5 C5 D5 | A1| B1| C1| D1| B5| C5| D5|
2 A2 B2 C2 D2 NaN NaN NaN | A2| B2| C2| D2|null|null|null|
3 A3 B3 C3 D3 NaN NaN NaN | A3| B3| C3| D3|null|null|null|
4 A6 NaN NaN NaN B6 C6 D6 | A6|null|null|null| B6| C6| D6|
5 A7 NaN NaN NaN B7 C7 D7 | A7|null|null|null| B7| C7| D7|
+---+----+----+----+----+----+----+
9.15. Concat Columns¶
my_list = [('a', 2, 3),
('b', 5, 6),
('c', 8, 9),
('a', 2, 3),
('b', 5, 6),
('c', 8, 9)]
col_name = ['col1', 'col2', 'col3']
#
pandas_df = pd.DataFrame(my_list,columns=col_name)
spark_df = spark.createDataFrame(my_list,schema=col_name)
col1 col2 col3
0 a 2 3
1 b 5 6
2 c 8 9
3 a 2 3
4 b 5 6
5 c 8 9
:: Python Code:
# one (or both) of the columns are not string typed, convert it (them) first to string,
pandas_df['concat'] = pandas_df['col1'] + pandas_df['col2'].astype(str)
# alternatively
pandas_df['concat'] = pandas_df.apply(lambda x:'%s%s'%(x['col1'],x['col2']),axis=1)
# don't use pandas_df['concat'] = pandas_df[['col1', 'col2']].apply(lambda x: ''.join(x), axis=1)
# note it will error out as expecting both fields are strings
#
pandas_df
#
spark_df.withColumn('concat',F.concat('col1','col2')).show()
:: Comparison:
+----+----+----+------+
|col1|col2|col3|concat|
col1 col2 col3 concat +----+----+----+------+
0 a 2 3 a2 | a| 2| 3| a2|
1 b 5 6 b5 | b| 5| 6| b5|
2 c 8 9 c8 | c| 8| 9| c8|
3 a 2 3 a2 | a| 2| 3| a2|
4 b 5 6 b5 | b| 5| 6| b5|
5 c 8 9 c8 | c| 8| 9| c8|
+----+----+----+------+
9.16. GroupBy¶
:: Python Code:
pandas_df.groupby(['col1']).agg({'col2':'min','col3':'mean'})
#
spark_df.groupBy(['col1']).agg({'col2': 'min', 'col3': 'avg'}).show()
:: Comparison:
+----+---------+---------+
col2 col3 |col1|min(col2)|avg(col3)|
col1 +----+---------+---------+
a 2 3 | c| 8| 9.0|
b 5 6 | b| 5| 6.0|
c 8 9 | a| 2| 3.0|
+----+---------+---------+
9.17. Pivot¶
:: Python Code:
pd.pivot_table(pandas_df, values='col3', index='col1', columns='col2', aggfunc=np.sum)
#
spark_df.groupBy(['col1']).pivot('col2').sum('col3').show()
:: Comparison:
+----+----+----+----+
col2 2 5 8 |col1| 2| 5| 8|
col1 +----+----+----+----+
a 6.0 NaN NaN | c|null|null| 18|
b NaN 12.0 NaN | b|null| 12|null|
c NaN NaN 18.0 | a| 6|null|null|
+----+----+----+----+
9.18. Unixtime to Date¶
from datetime import datetime
my_list = [['a', int("1284101485")], ['b', int("2284101485")],['c', int("3284101485")]]
col_name = ['A', 'ts']
pandas_df = pd.DataFrame(my_list,columns=col_name)
spark_df = spark.createDataFrame(pandas_df)
:: Python Code:
pandas_df['datetime'] = pd.to_datetime(pandas_df['ts'], unit='s').dt.tz_localize('UTC')
pandas_df
spark.conf.set("spark.sql.session.timeZone", "UTC")
from pyspark.sql.types import DateType
spark_df.withColumn('date', F.from_unixtime('ts')).show() #.cast(DateType())
:: Comparison:
+---+----------+-------------------+
| A| ts| date|
A ts datetime +---+----------+-------------------+
0 a 1284101485 2010-09-10 06:51:25+00:00 | a|1284101485|2010-09-10 06:51:25|
1 b 2284101485 2042-05-19 08:38:05+00:00 | b|2284101485|2042-05-19 08:38:05|
2 c 3284101485 2074-01-25 10:24:45+00:00 | c|3284101485|2074-01-25 10:24:45|
+---+----------+-------------------+