diff --git a/README.md b/README.md index f8bd71182..aa25f038d 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,8 @@ [![built_by iron](https://img.shields.io/badge/built_by-iron-FF69A4.svg)](http://ironmussa.com) [![Updates](https://pyup.io/repos/github/ironmussa/Optimus/shield.svg)](https://pyup.io/repos/github/ironmussa/Optimus/) [![GitHub release](https://img.shields.io/github/release/ironmussa/optimus.svg)](https://github.com/ironmussa/Optimus/) [![Codacy Badge](https://api.codacy.com/project/badge/Grade/e01572e2af5640fcbcdd58e7408f3ea0)](https://www.codacy.com/app/favio.vazquezp/Optimus?utm_source=github.com&utm_medium=referral&utm_content=ironmussa/Optimus&utm_campaign=badger) [![StackShare](https://img.shields.io/badge/tech-stack-0690fa.svg?style=flat)](https://stackshare.io/iron-mussa/devops) -[![Platforms](https://img.shields.io/badge/platform-Linux%20%7C%20Mac%20OS%20%7C%20Windows-blue.svg)](https://spark.apache.org/docs/2.2.0/#downloading) [![Dependency Status](https://gemnasium.com/badges/github.com/ironmussa/Optimus.svg)](https://gemnasium.com/github.com/ironmussa/Optimus) [![Quality Gate](https://sonarqube.com/api/badges/gate?key=ironmussa-optimus:optimus)](https://sonarqube.com/dashboard/index/ironmussa-optimus:optimus) +[![Platforms](https://img.shields.io/badge/platform-Linux%20%7C%20Mac%20OS%20%7C%20Windows-blue.svg)](https://spark.apache.org/docs/2.2.0/#downloading) [![Dependency Status](https://gemnasium.com/badges/github.com/ironmussa/Optimus.svg)](https://gemnasium.com/github.com/ironmussa/Optimus) [![Quality Gate](https://sonarqube.com/api/badges/gate?key=ironmussa-optimus:optimus)](https://sonarqube.com/dashboard/index/ironmussa-optimus:optimus) [![Code Health](https://landscape.io/github/ironmussa/Optimus/develop/landscape.svg?style=flat)](https://landscape.io/github/ironmussa/Optimus/develop) + [![Join the chat at https://gitter.im/optimuspyspark/Lobby](https://badges.gitter.im/optimuspyspark/Lobby.svg)](https://gitter.im/optimuspyspark/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) diff --git a/docs/index.rst b/docs/index.rst index cd80f1990..fe2edd8ac 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -108,7 +108,7 @@ Lets assume you have the following dataset, called foo.csv, in your current dire # case, local file system (hard drive of the pc) is used. filePath = "file:///" + os.getcwd() + "/foo.csv" - df = tools.read_dataset_csv(path=filePath, + df = tools.read_csv(path=filePath, delimiter_mark=',') # Instance of profiler class @@ -135,6 +135,7 @@ dataFrames. - DataFrameAnalyzer.get_numerical_hist(df_one_col, num_bars) - DataFrameAnalyzer.unique_values_col(column) - DataFrameAnalyzer.write_json(json_cols, path_to_json_file) +- DataFrameAnalyzer.get_frequency(columns, sort_by_count=True) Lets assume you have the following dataset, called foo.csv, in your current directory: @@ -180,23 +181,23 @@ Lets assume you have the following dataset, called foo.csv, in your current dire | 19 | JAMES | Chadwick | 467 | null | 10 | 1921/05/03 | # | +----+----------------------+-------------+-----------+------------+-------+------------+----------+ -The following code shows how to instanciate the class to analyze a dataFrame: +The following code shows how to instantiate the class to analyze a dataFrame: .. code:: python # Import optimus import optimus as op # Instance of Utilities class - tools = op.Utilites() + tools = op.Utilities() # Reading dataframe. os.getcwd() returns de current directory of the notebook # 'file:///' is a prefix that specifies the type of file system used, in this # case, local file system (hard drive of the pc) is used. filePath = "file:///" + os.getcwd() + "/foo.csv" - df = tools.read_dataset_csv(path=filePath, delimiter_mark=',') + df = tools.read_csv(path=filePath, delimiter_mark=',') - analyzer = op.DataFrameAnalizer(df=df,pathFile=filePath) + analyzer = op.DataFrameAnalyzer(df=df,pathFile=filePath) Methods -------- @@ -534,7 +535,7 @@ Example: Analyzer.write_json(json_cols, path_to_json_file) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -This functions ... and outputs a JSON in the specified path. +This functions outputs a JSON for the DataFrame in the specified path. Input: @@ -556,6 +557,101 @@ Example: analyzer.write_json(json_cols=json_cols, path_to_json_file= os.getcwd() + "/foo.json") +Analyzer.get_frequency(self, columns, sort_by_count=True) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +This function gets the frequencies for values inside the specified columns. + +Input: + +``columns``: String or List of columns to analyze + +``sort_by_count``: Boolean if true the counts will be sort desc. + +The method outputs a Spark Dataframe with counts per existing values in each column. + +Tu use it, first lets create a sample DataFrame: + +.. code:: python + + import random + import optimus as op + from pyspark.sql.types import StringType, StructType, IntegerType, FloatType, DoubleType, StructField + + schema = StructType( + [ + StructField("strings", StringType(), True), + StructField("integers", IntegerType(), True), + StructField("integers2", IntegerType(), True), + StructField("floats", FloatType(), True), + StructField("double", DoubleType(), True) + ] + ) + + size = 200 + # Generating strings column: + foods = [' pizza! ', 'pizza', 'PIZZA;', 'pizza', 'pízza¡', 'Pizza', 'Piz;za'] + foods = [foods[random.randint(0,6)] for count in range(size)] + # Generating integer column: + num_col_1 = [random.randint(0,9) for number in range(size)] + # Generating integer column: + num_col_2 = [random.randint(0,9) for number in range(size)] + # Generating integer column: + num_col_3 = [random.random() for number in range(size)] + # Generating integer column: + num_col_4 = [random.random() for number in range(size)] + + # Building DataFrame + df = op.spark.createDataFrame(list(zip(foods, num_col_1, num_col_2, num_col_3, num_col_4)),schema=schema) + + # Instantiate Analyzer + analyzer = op.DataFrameAnalyzer(df) + + # Get frequency DataFrame + df_counts = analyzer.get_frequency(["strings", "integers"], True) + +And you will get (note that these are random generated values): + ++-----------------+-----+ +| strings|count| ++-----------------+-----+ +| pizza| 48| ++-----------------+-----+ +| Piz;za| 38| ++-----------------+-----+ +| Pizza| 37| ++-----------------+-----+ +| pízza¡| 29| ++-----------------+-----+ +| pizza! | 25| ++-----------------+-----+ +| PIZZA;| 23| ++-----------------+-----+ + ++--------+-----+ +|integers|count| ++--------+-----+ +| 8| 31| ++--------+-----+ +| 5| 24| ++--------+-----+ +| 1| 24| ++--------+-----+ +| 9| 20| ++--------+-----+ +| 6| 20| ++--------+-----+ +| 2| 19| ++--------+-----+ +| 3| 19| ++--------+-----+ +| 0| 17| ++--------+-----+ +| 4| 14| ++--------+-----+ +| 7| 12| ++--------+-----+ + DataFrameTransformer class -------------------------- @@ -589,7 +685,7 @@ DataFrameTransformer class - DataFrameTransformer.set_col(columns, func, dataType) * **Others**: - - DataFrameTransformer.explode_table(coldId, col, new_col_feature) + - DataFrameTransformer.count_items(col_id, col_search, new_col_feature, search_string) - DataFrameTransformer.age_calculate(column) DataFrameTransformer class receives a dataFrame as an argument. This diff --git a/examples/Impute_Missing_Data_With_Optimus.ipynb b/examples/Impute_Missing_Data_With_Optimus.ipynb index 49d878bc1..bdcb33be7 100644 --- a/examples/Impute_Missing_Data_With_Optimus.ipynb +++ b/examples/Impute_Missing_Data_With_Optimus.ipynb @@ -74,9 +74,7 @@ ], "source": [ "# Import optimus\n", - "import optimus as op\n", - "# Import os for reading from local\n", - "import os" + "import optimus as op" ] }, { @@ -105,8 +103,7 @@ }, "outputs": [], "source": [ - "path = \"file:///\" + os.getcwd() + \"/impute_data.csv\"\n", - "df = tools.read_dataset_csv(path, delimiter_mark=\",\", header=\"true\")" + "df = tools.read_csv(\"impute_data.csv\", delimiter_mark=\",\", header=\"true\")" ] }, { @@ -461,17 +458,17 @@ " \n", " \n", "\n", - "
\n", + "
\n", " \n", "\n", "
\n", "
\n", - " \n", " Toggle details\n", " \n", "
\n", - "
\n", + "
\n", "
\n", "

Quantile statistics

\n", " \n", @@ -619,7 +616,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 6, diff --git a/examples/Optimus_Example.ipynb b/examples/Optimus_Example.ipynb index 976f4641e..56eacdb78 100644 --- a/examples/Optimus_Example.ipynb +++ b/examples/Optimus_Example.ipynb @@ -92,9 +92,7 @@ ], "source": [ "# Import optimus\n", - "import optimus as op\n", - "# Import module for system tools \n", - "import os" + "import optimus as op" ] }, { @@ -112,17 +110,6 @@ "collapsed": true }, "outputs": [], - "source": [ - "filePath = \"file:///\" + os.getcwd() + \"/foo.csv\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], "source": [ "# Instance of Utilities class\n", "tools = op.Utilities()" @@ -137,19 +124,16 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "# Reading dataframe. os.getcwd() returns de current directory of the notebook \n", - "# 'file:///' is a prefix that specifies the type of file system used, in this\n", - "# case, local file system (hard drive of the pc) is used.\n", - "filePath = \"file:///\" + os.getcwd() + \"/foo.csv\"\n", + "# Reading dataframe in this case, local file \n", + "# system (hard drive of the pc) is used.\n", "\n", - "df = tools.read_dataset_csv(path=filePath,\n", - " delimiter_mark=',')" + "df = tools.read_csv(path=\"foo.csv\", delimiter_mark=',')" ] }, { @@ -170,7 +154,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "scrolled": false }, @@ -367,11 +351,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -394,7 +378,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -402,7 +386,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -413,7 +397,7 @@ " \n", "
\n", "

Warnings

\n", - "
  • id has 3 / 15.0% missing values Missing
\n", + "
\n", "
\n", "\n", "
\n", @@ -434,7 +418,7 @@ "
\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -460,7 +444,7 @@ "\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -478,17 +462,17 @@ " \n", " \n", "\n", - "
\n", - " \n", + "
\n", + " \n", "\n", "
\n", "
\n", - " \n", " Toggle details\n", " \n", "
\n", - "
\n", + "
\n", "
\n", "

Quantile statistics

\n", "
Number of observations20 19
Total Missing (%)1.9% 0.0%
Total size in memory
Categorical5 2
Date
Text (Unique)0 3
Rejected
Unique (%)95.0%100.0%
Missing (%)
Mean551.55556
Minimum
\n", @@ -498,23 +482,23 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -526,42 +510,42 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", "
5-th percentile115.75115.5
Q1398373
Median552553
Q3754.25773.5
95-th percentile916920
Maximum
Interquartile range356.25400.5
\n", "

Descriptive statistics

\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -570,23 +554,155 @@ "
Standard deviation273.45280.2
Coef of variation0.495780.50395
Kurtosis-0.9722-1.0412
Mean551.55556
MAD217.95225.05
Skewness-0.16928-0.2137
Sum1103110564
Variance7477478511
Memory size
\n", "
\n", "
\n", - " \n", + " \n", "
\n", "
\n", "
\n", "
\n", "

birth
\n", + " Categorical, Unique\n", + "

\n", + "
\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
First 3 values
2000/03/22
1958/03/26
1980/07/07
\n", + "
\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Last 3 values
1930/08/12
1990/07/11
1970/07/13
\n", + "
\n", + " \n", + " Toggle details\n", + " \n", + "
\n", + "
\n", + "

First 20 values

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
12000/03/22
21958/03/26
31980/07/07
41950/07/14
51993/12/08
61950/07/08
71899/01/01
81923/03/12
91994/01/04
101954/07/10
111956/11/30
121920/04/22
131997/06/27
141999/02/15
151921/05/03
161990/07/09
171930/08/12
181990/07/11
191970/07/13
\n", + "

Last 20 values

\n", + " \n", + "
\n", + "
\n", + "
\n", + "

dummyCol
\n", " Categorical\n", "

\n", "
\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -606,41 +722,41 @@ " \n", "
Distinct count1913
Unique (%)95.0%68.4%
Missing (%)
\n", "
\n", - "
\n", + "
\n", " \n", " \n", - " \n", + " \n", " \n", "\n", - " \n", + " \n", " \n", "\n", - " \n", + " \n", " \n", "\n", - " \n", + " \n", " \n", @@ -648,12 +764,12 @@ "
1921/05/03you\n", - "
\n", - "  \n", + "
\n", + " 3\n", "
\n", - " 2\n", + " \n", "
1950/07/08gonna\n", - "
\n", - "  \n", + "
\n", + " 3\n", "
\n", - " 1\n", + " \n", "
1958/03/26never\n", - "
\n", + "
\n", "  \n", "
\n", - " 1\n", + " 2\n", "
Other values (16)Other values (10)\n", "
\n", - " 16\n", + " data-delay=500 title=\"Percentage: 57.9%\">\n", + " 11\n", "
\n", " \n", "
\n", "
\n", "
\n", - " \n", " Toggle details\n", " \n", "
\n", - "
\n", + "
\n", " \n", " \n", " \n", @@ -664,551 +780,231 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", "\n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", "\n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", "\n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", "\n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", "\n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", "\n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", "\n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", "\n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", "\n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", "\n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", "\n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", "
1921/05/03210.0%you315.8%\n", "
 
\n", "
1950/07/0815.0%\n", - "
 
\n", - "
1958/03/2615.0%\n", - "
 
\n", - "
1923/03/1215.0%\n", - "
 
\n", - "
1990/07/0915.0%\n", - "
 
\n", - "
1990/07/1115.0%\n", - "
 
\n", - "
1956/11/3015.0%\n", - "
 
\n", - "
1994/01/0415.0%gonna315.8%\n", - "
 
\n", + "
 
\n", "
1920/04/2215.0%never210.5%\n", - "
 
\n", + "
 
\n", "
2000/03/2215.0%#210.5%\n", - "
 
\n", + "
 
\n", "
1970/07/13never 15.0%5.3%\n", - "
 
\n", + "
 
\n", "
1950/07/14down15.0%5.3%\n", - "
 
\n", + "
 
\n", "
1899/01/01run 15.0%5.3%\n", - "
 
\n", + "
 
\n", "
1980/07/07desert15.0%5.3%\n", - "
 
\n", + "
 
\n", "
1993/12/08up15.0%5.3%\n", - "
 
\n", + "
 
\n", "
1999/02/15and15.0%5.3%\n", - "
 
\n", + "
 
\n", "
1997/06/27around15.0%5.3%\n", - "
 
\n", + "
 
\n", "
1930/08/12let15.0%5.3%\n", - "
 
\n", + "
 
\n", "
1954/07/10give15.0%5.3%\n", - "
 
\n", + "
 
\n", "
\n", "
\n", "
\n", "
\n", - "

dummyCol
\n", - " Categorical\n", + "

firstName
\n", + " Categorical, Unique\n", "

\n", - "
\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Distinct count13
Unique (%)65.0%
Missing (%)0.0%
Missing (n)0
Infinite (%)0.0%
Infinite (n)0
\n", + "
\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
First 3 values
JaMES
Marie
CaRL
\n", + "
\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Last 3 values
Fred
David
PAUL
\n", + "
\n", + " \n", + " Toggle details\n", + " \n", "
\n", - "
\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - "\n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - "
you\n", - "
\n", - " 3\n", - "
\n", - " \n", - "
gonna\n", - "
\n", - " 3\n", - "
\n", - " \n", - "
#\n", - "
\n", - " 3\n", - "
\n", - " \n", - "
Other values (10)\n", - "
\n", - " 11\n", - "
\n", - " \n", - "
\n", - "
\n", - "
\n", - " \n", - " Toggle details\n", - " \n", - "
\n", - "
\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ValueCountFrequency (%) 
you315.0%\n", - "
 
\n", - "
gonna315.0%\n", - "
 
\n", - "
#315.0%\n", - "
 
\n", - "
never210.0%\n", - "
 
\n", - "
never 15.0%\n", - "
 
\n", - "
down15.0%\n", - "
 
\n", - "
run 15.0%\n", - "
 
\n", - "
desert15.0%\n", - "
 
\n", - "
up15.0%\n", - "
 
\n", - "
and15.0%\n", - "
 
\n", - "
around15.0%\n", - "
 
\n", - "
let15.0%\n", - "
 
\n", - "
give15.0%\n", - "
 
\n", - "
\n", - "
\n", - "
\n", - "
\n", - "

firstName
\n", - " Categorical\n", - "

\n", - "
\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Distinct count19
Unique (%)95.0%
Missing (%)0.0%
Missing (n)0
Infinite (%)0.0%
Infinite (n)0
\n", - "
\n", - "
\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - "\n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - "
JAMES\n", - "
\n", - "  \n", - "
\n", - " 2\n", - "
Galileo\n", - "
\n", - "  \n", - "
\n", - " 1\n", - "
PAUL\n", - "
\n", - "  \n", - "
\n", - " 1\n", - "
Other values (16)\n", - "
\n", - " 16\n", - "
\n", - " \n", - "
\n", - "
\n", - "
\n", - " \n", - " Toggle details\n", - " \n", + "
\n", + "

First 20 values

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
1JaMES
2Marie
3CaRL
4André
5Johannes
6Luis
7William
8Isaac
9Albert
10Emmy%%
11Galileo
12((( Heinrich )))))
13JAMES
14Arthur
15Max!!!
16NiELS
17Fred
18David
19PAUL
\n", + "

Last 20 values

\n", + " \n", "
\n", - "
\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ValueCountFrequency (%) 
JAMES210.0%\n", - "
 
\n", - "
Galileo15.0%\n", - "
 
\n", - "
PAUL15.0%\n", - "
 
\n", - "
David15.0%\n", - "
 
\n", - "
Marie15.0%\n", - "
 
\n", - "
Emmy%%15.0%\n", - "
 
\n", - "
Isaac15.0%\n", - "
 
\n", - "
Luis15.0%\n", - "
 
\n", - "
Johannes15.0%\n", - "
 
\n", - "
Arthur15.0%\n", - "
 
\n", - "
André15.0%\n", - "
 
\n", - "
((( Heinrich )))))15.0%\n", - "
 
\n", - "
CaRL15.0%\n", - "
 
\n", - "
Albert15.0%\n", - "
 
\n", - "
NiELS15.0%\n", - "
 
\n", - "
Max!!!15.0%\n", - "
 
\n", - "
Fred15.0%\n", - "
 
\n", - "
JaMES15.0%\n", - "
 
\n", - "
William15.0%\n", - "
 
\n", - "
\n", - "
\n", "
\n", "
\n", "

id
\n", @@ -1220,19 +1016,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1250,7 +1046,7 @@ "\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1268,17 +1064,17 @@ " \n", " \n", "\n", - "
\n", - " \n", + "
\n", + " \n", "\n", "
\n", "
\n", - " \n", " Toggle details\n", " \n", "
\n", - "
\n", + "
\n", "
\n", "

Quantile statistics

\n", "
Distinct count1619
Unique (%)94.1%100.0%
Missing (%)15.0%0.0%
Missing (n)30
Infinite (%)
Mean10.88210
Minimum
\n", @@ -1288,23 +1084,23 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1316,42 +1112,42 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", "
5-th percentile1.81.9
Q165.5
Median1210
Q31614.5
95-th percentile1918.1
Maximum
Interquartile range109
\n", "

Descriptive statistics

\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1360,235 +1156,141 @@ "
Standard deviation6.16325.6273
Coef of variation0.566350.56273
Kurtosis-1.324-1.2067
Mean10.88210
MAD5.3014.7368
Skewness-0.208990
Sum185190
Variance37.98531.667
Memory size
\n", "
\n", "
\n", - " \n", + " \n", "
\n", "
\n", "
\n", "
\n", "

lastName
\n", - " Categorical\n", + " Categorical, Unique\n", "

\n", - "
\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Distinct count19
Unique (%)95.0%
Missing (%)0.0%
Missing (n)0
Infinite (%)0.0%
Infinite (n)0
\n", - "
\n", - "
\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - "\n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - "
Chadwick\n", - "
\n", - "  \n", - "
\n", - " 2\n", - "
Nöether$\n", - "
\n", - "  \n", - "
\n", - " 1\n", - "
KEPLER\n", - "
\n", - "  \n", - "
\n", - " 1\n", - "
Other values (16)\n", - "
\n", - " 16\n", - "
\n", - " \n", - "
\n", - "
\n", + "
\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
First 3 values
Chadwick
Gilbert###
Alvarez$$%!
\n", + "
\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Last 3 values
Ampère
Planck!!!
CURIE
\n", "
\n", - " \n", + " \n", " Toggle details\n", " \n", "
\n", - "
\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ValueCountFrequency (%) 
Chadwick210.0%\n", - "
 
\n", - "
Nöether$15.0%\n", - "
 
\n", - "
KEPLER15.0%\n", - "
 
\n", - "
CURIE15.0%\n", - "
 
\n", - "
Böhr//((%%15.0%\n", - "
 
\n", - "
Hoy&&&le15.0%\n", - "
 
\n", - "
Alvarez$$%!15.0%\n", - "
 
\n", - "
Einstein15.0%\n", - "
 
\n", - "
GALiLEI15.0%\n", - "
 
\n", - "
Gilbert###15.0%\n", - "
 
\n", - "
Hertz15.0%\n", - "
 
\n", - "
Ga%%%uss15.0%\n", - "
 
\n", - "
H$$$ilbert15.0%\n", - "
 
\n", - "
M$$ax%%well15.0%\n", - "
 
\n", - "
Newton15.0%\n", - "
 
\n", - "
Ampère15.0%\n", - "
 
\n", - "
dirac$15.0%\n", - "
 
\n", - "
COM%%%pton15.0%\n", - "
 
\n", - "
Planck!!!15.0%\n", - "
 
\n", - "
\n", - "
\n", + "
\n", + "

First 20 values

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
1Chadwick
2Gilbert###
3Alvarez$$%!
4Einstein
5Hertz
6Hoy&&&le
7Nöether$
8Ga%%%uss
9Böhr//((%%
10dirac$
11M$$ax%%well
12H$$$ilbert
13Newton
14COM%%%pton
15GALiLEI
16KEPLER
17Ampère
18Planck!!!
19CURIE
\n", + "

Last 20 values

\n", + " \n", + "
\n", "
\n", "
\n", "

price
\n", @@ -1604,7 +1306,7 @@ " \n", " \n", " Unique (%)\n", - " 40.0%\n", + " 42.1%\n", " \n", " \n", " Missing (%)\n", @@ -1630,7 +1332,7 @@ "\n", " \n", " Mean\n", - " 6.25\n", + " 6.0526\n", " \n", " \n", " Minimum\n", @@ -1648,17 +1350,17 @@ "

\n", "
\n", "
\n", - "
\n", - " \n", + "
\n", + " \n", "\n", "
\n", "
\n", - " \n", " Toggle details\n", " \n", "
\n", - "
\n", + "
\n", "
\n", "

Quantile statistics

\n", " \n", @@ -1668,7 +1370,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1680,7 +1382,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1696,42 +1398,42 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", "
5-th percentile1.951.9
Q1
Q38.258
95-th percentile
Interquartile range5.255
\n", "

Descriptive statistics

\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1740,7 +1442,7 @@ "
Standard deviation3.00662.9528
Coef of variation0.481050.48786
Kurtosis-1.4225-1.4482
Mean6.256.0526
MAD2.7252.6814
Skewness-0.28238-0.22564
Sum125115
Variance9.03958.7193
Memory size
\n", "
\n", "
\n", - " \n", + " \n", "
\n", "
\n", "
\n", @@ -1756,7 +1458,7 @@ " \n", " \n", " Unique (%)\n", - " 65.0%\n", + " 68.4%\n", " \n", " \n", " Missing (%)\n", @@ -1776,13 +1478,13 @@ " \n", " \n", "
\n", - "
\n", + "
\n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", - " \n", + " \n", " \n", " \n", @@ -1818,12 +1520,12 @@ "
pizza\n", - "
\n", + "
\n", " 4\n", "
\n", " \n", @@ -1790,17 +1492,17 @@ "
taco\n", - "
\n", + "
\n", " 3\n", "
\n", " \n", "
nullpasta\n", - "
\n", + "
\n", "  \n", "
\n", " 2\n", @@ -1809,8 +1511,8 @@ "
Other values (10)\n", "
\n", - " 11\n", + " data-delay=500 title=\"Percentage: 52.6%\">\n", + " 10\n", "
\n", " \n", "
\n", "
\n", "
\n", - " \n", " Toggle details\n", " \n", "
\n", - "
\n", + "
\n", " \n", " \n", " \n", @@ -1836,91 +1538,91 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", "\n", " \n", " \n", - " \n", + " \n", " \n", "\n", - " \n", - " \n", - " \n", - " \n", - "\n", " \n", " \n", - " \n", + " \n", " \n", "\n", " \n", " \n", - " \n", + " \n", " \n", "\n", " \n", " \n", - " \n", + " \n", " \n", "\n", " \n", " \n", - " \n", + " \n", " \n", "\n", " \n", " \n", - " \n", + " \n", " \n", "\n", " \n", " \n", - " \n", + " \n", " \n", "\n", " \n", " \n", - " \n", + " \n", " \n", "\n", " \n", " \n", - " \n", + " \n", " \n", "\n", " \n", " \n", - " \n", + " \n", " \n", "\n", " \n", " \n", - " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", " \n", @@ -1950,7 +1652,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1961,7 +1663,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1972,7 +1674,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1983,7 +1685,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1994,7 +1696,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2010,10 +1712,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -2040,14 +1742,12 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, + "execution_count": 5, + "metadata": {}, "outputs": [], "source": [ "# Instance of analyzer class\n", - "analyzer = op.DataFrameAnalyzer(df=df, path_file=filePath)" + "analyzer = op.DataFrameAnalyzer(df=df)" ] }, { @@ -2081,7 +1781,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "scrolled": false }, @@ -2089,10 +1789,10 @@ { "data": { "text/html": [ - "
pizza420.0%21.1%\n", "
 
\n", "
taco315.0%15.8%\n", "
 
\n", "
null210.0%\n", - "
 
\n", - "
pasta210.0%10.5%\n", "
 
\n", "
taaaccoo15.0%5.3%\n", "
 
\n", "
piza15.0%5.3%\n", "
 
\n", "
hamburguer15.0%5.3%\n", "
 
\n", "
BEER15.0%5.3%\n", "
 
\n", "
pizzza15.0%5.3%\n", "
 
\n", "
arepa15.0%5.3%\n", "
 
\n", "
Rice15.0%5.3%\n", "
 
\n", "
11079015.0%5.3%\n", "
 
\n", "
Cake15.0%5.3%\n", + "
 
\n", + "
null15.3%\n", "
 
\n", "
01.01LuisAlvarez$$%!123
12.02AndréAmpère423
23.03NiELSBöhr//((%%551
34.04PAULdirac$521
4NaN5AlbertEinstein634
Column name: id
Column datatype: int
DatatypeQuantityPercentage
None315.00 %
Empty str00.00 %
String00.00 %
Integer1785.00 %
Float00.00 %
" + "
Column name: id
Column datatype: int
DatatypeQuantityPercentage
None00.00 %
Empty str00.00 %
String00.00 %
Integer19100.00 %
Float00.00 %
" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2109,7 +1809,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2119,16 +1819,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "end of __analyze 3.2843620777130127\n" + "end of __analyze 4.712668180465698\n" ] }, { "data": { "text/html": [ - "
Column name: firstName
Column datatype: string
DatatypeQuantityPercentage
None00.00 %
Empty str00.00 %
String20100.00 %
Integer00.00 %
Float00.00 %
" + "
Column name: firstName
Column datatype: string
DatatypeQuantityPercentage
None00.00 %
Empty str00.00 %
String19100.00 %
Integer00.00 %
Float00.00 %
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Column name: dummyCol
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" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2321,7 +2021,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2331,17 +2031,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "end of __analyze 1.5880918502807617\n", - "Total execution time: 14.563560009002686\n" + "end of __analyze 1.47003173828125\n", + "Total execution time: 20.0109121799469\n" ] }, { "data": { "text/html": [ - "" + "
General description
FeaturesName or Quantity
File Namefoo.csv
Columns8
Rows20
" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2360,6 +2060,33 @@ "string columns with possible numbers on them and None and empty string values in columns." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also plot histograms for individual columns using the `plot_hist` function. This is an interesting feature because you are plotting from a Spark Dataframe:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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PAABb+/jjZXrxxSxdfnn3/369XGPHPqNf/vJ6SdLVV1+riy5qr5demkjwRNCr96n20tJS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\nAIAi3g3a2OLc2CN0AAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "analyzer.plot_hist(\"price\",\"numerical\")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -2390,20 +2117,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "+----+---------+--------------------+---------+--------+-----+----------+--------+\n", - "| id|firstName| lastName|billingId| product|price| birth|dummyCol|\n", - "+----+---------+--------------------+---------+--------+-----+----------+--------+\n", - "| 1| Luis| Alvarez$$%!| 123| Cake| 10|1980/07/07| never|\n", - "| 2| André| Ampère| 423| piza| 8|1950/07/08| gonna|\n", - "| 3| NiELS| Böhr//((%%| 551| pizza| 8|1990/07/09| give|\n", - "| 4| PAUL| dirac$| 521| pizza| 8|1954/07/10| you|\n", - "|null| Albert| Einstein| 634| pizza| 8|1990/07/11| up|\n", - "| 6| Galileo| GALiLEI| 672| arepa| 5|1930/08/12| never|\n", - "| 7| CaRL| Ga%%%uss| 323| taco| 3|1970/07/13| gonna|\n", - "|null| David| H$$$ilbert| 624|taaaccoo| 3|1950/07/14| let|\n", - "| 9| Johannes| KEPLER| 735| taco| 3|1920/04/22| you|\n", - "| 10| JaMES| M$$ax%%well| 875| taco| 3|1923/03/12| down|\n", - "+----+---------+--------------------+---------+--------+-----+----------+--------+\n", + "+---+---------+--------------------+---------+--------+-----+----------+--------+\n", + "| id|firstName| lastName|billingId| product|price| birth|dummyCol|\n", + "+---+---------+--------------------+---------+--------+-----+----------+--------+\n", + "| 1| Luis| Alvarez$$%!| 123| Cake| 10|1980/07/07| never|\n", + "| 2| André| Ampère| 423| piza| 8|1950/07/08| gonna|\n", + "| 3| NiELS| Böhr//((%%| 551| pizza| 8|1990/07/09| give|\n", + "| 4| PAUL| dirac$| 521| pizza| 8|1954/07/10| you|\n", + "| 5| Albert| Einstein| 634| pizza| 8|1990/07/11| up|\n", + "| 6| Galileo| GALiLEI| 672| arepa| 5|1930/08/12| never|\n", + "| 7| CaRL| Ga%%%uss| 323| taco| 3|1970/07/13| gonna|\n", + "| 8| David| H$$$ilbert| 624|taaaccoo| 3|1950/07/14| let|\n", + "| 9| Johannes| KEPLER| 735| taco| 3|1920/04/22| you|\n", + "| 10| JaMES| M$$ax%%well| 875| taco| 3|1923/03/12| down|\n", + "+---+---------+--------------------+---------+--------+-----+----------+--------+\n", "only showing top 10 rows\n", "\n" ] @@ -2430,27 +2157,27 @@ "output_type": "stream", "text": [ "Original dataFrame:\n", - "+----+---------+-----------+---------+-------+-----+----------+--------+\n", - "| id|firstName| lastName|billingId|product|price| birth|dummyCol|\n", - "+----+---------+-----------+---------+-------+-----+----------+--------+\n", - "| 1| Luis|Alvarez$$%!| 123| Cake| 10|1980/07/07| never|\n", - "| 2| André| Ampère| 423| piza| 8|1950/07/08| gonna|\n", - "| 3| NiELS| Böhr//((%%| 551| pizza| 8|1990/07/09| give|\n", - "| 4| PAUL| dirac$| 521| pizza| 8|1954/07/10| you|\n", - "|null| Albert| Einstein| 634| pizza| 8|1990/07/11| up|\n", - "+----+---------+-----------+---------+-------+-----+----------+--------+\n", + "+---+---------+-----------+---------+-------+-----+----------+--------+\n", + "| id|firstName| lastName|billingId|product|price| birth|dummyCol|\n", + "+---+---------+-----------+---------+-------+-----+----------+--------+\n", + "| 1| Luis|Alvarez$$%!| 123| Cake| 10|1980/07/07| never|\n", + "| 2| André| Ampère| 423| piza| 8|1950/07/08| gonna|\n", + "| 3| NiELS| Böhr//((%%| 551| pizza| 8|1990/07/09| give|\n", + "| 4| PAUL| dirac$| 521| pizza| 8|1954/07/10| you|\n", + "| 5| Albert| Einstein| 634| pizza| 8|1990/07/11| up|\n", + "+---+---------+-----------+---------+-------+-----+----------+--------+\n", "only showing top 5 rows\n", "\n", "Trimmed dataFrame:\n", - "+----+---------+-----------+---------+-------+-----+----------+--------+\n", - "| id|firstName| lastName|billingId|product|price| birth|dummyCol|\n", - "+----+---------+-----------+---------+-------+-----+----------+--------+\n", - "| 1| Luis|Alvarez$$%!| 123| Cake| 10|1980/07/07| never|\n", - "| 2| André| Ampère| 423| piza| 8|1950/07/08| gonna|\n", - "| 3| NiELS| Böhr//((%%| 551| pizza| 8|1990/07/09| give|\n", - "| 4| PAUL| dirac$| 521| pizza| 8|1954/07/10| you|\n", - "|null| Albert| Einstein| 634| pizza| 8|1990/07/11| up|\n", - "+----+---------+-----------+---------+-------+-----+----------+--------+\n", + "+---+---------+-----------+---------+-------+-----+----------+--------+\n", + "| id|firstName| lastName|billingId|product|price| birth|dummyCol|\n", + "+---+---------+-----------+---------+-------+-----+----------+--------+\n", + "| 1| Luis|Alvarez$$%!| 123| Cake| 10|1980/07/07| never|\n", + "| 2| André| Ampère| 423| piza| 8|1950/07/08| gonna|\n", + "| 3| NiELS| Böhr//((%%| 551| pizza| 8|1990/07/09| give|\n", + "| 4| PAUL| dirac$| 521| pizza| 8|1954/07/10| you|\n", + "| 5| Albert| Einstein| 634| pizza| 8|1990/07/11| up|\n", + "+---+---------+-----------+---------+-------+-----+----------+--------+\n", "only showing top 5 rows\n", "\n" ] @@ -2486,27 +2213,27 @@ "output_type": "stream", "text": [ "Original dataFrame:\n", - "+----+---------+-----------+---------+-------+-----+----------+--------+\n", - "| id|firstName| lastName|billingId|product|price| birth|dummyCol|\n", - "+----+---------+-----------+---------+-------+-----+----------+--------+\n", - "| 1| Luis|Alvarez$$%!| 123| Cake| 10|1980/07/07| never|\n", - "| 2| André| Ampère| 423| piza| 8|1950/07/08| gonna|\n", - "| 3| NiELS| Böhr//((%%| 551| pizza| 8|1990/07/09| give|\n", - "| 4| PAUL| dirac$| 521| pizza| 8|1954/07/10| you|\n", - "|null| Albert| Einstein| 634| pizza| 8|1990/07/11| up|\n", - "+----+---------+-----------+---------+-------+-----+----------+--------+\n", + "+---+---------+-----------+---------+-------+-----+----------+--------+\n", + "| id|firstName| lastName|billingId|product|price| birth|dummyCol|\n", + "+---+---------+-----------+---------+-------+-----+----------+--------+\n", + "| 1| Luis|Alvarez$$%!| 123| Cake| 10|1980/07/07| never|\n", + "| 2| André| Ampère| 423| piza| 8|1950/07/08| gonna|\n", + "| 3| NiELS| Böhr//((%%| 551| pizza| 8|1990/07/09| give|\n", + "| 4| PAUL| dirac$| 521| pizza| 8|1954/07/10| you|\n", + "| 5| Albert| Einstein| 634| pizza| 8|1990/07/11| up|\n", + "+---+---------+-----------+---------+-------+-----+----------+--------+\n", "only showing top 5 rows\n", "\n", "Removing special chars and accents dataFrame:\n", - "+----+---------+--------+---------+-------+-----+--------+--------+\n", - "| id|firstName|lastName|billingId|product|price| birth|dummyCol|\n", - "+----+---------+--------+---------+-------+-----+--------+--------+\n", - "| 1| Luis| Alvarez| 123| Cake| 10|19800707| never|\n", - "| 2| Andre| Ampere| 423| piza| 8|19500708| gonna|\n", - "| 3| NiELS| Bohr| 551| pizza| 8|19900709| give|\n", - "| 4| PAUL| dirac| 521| pizza| 8|19540710| you|\n", - "|null| Albert|Einstein| 634| pizza| 8|19900711| up|\n", - "+----+---------+--------+---------+-------+-----+--------+--------+\n", + "+---+---------+--------+---------+-------+-----+--------+--------+\n", + "| id|firstName|lastName|billingId|product|price| birth|dummyCol|\n", + "+---+---------+--------+---------+-------+-----+--------+--------+\n", + "| 1| Luis| Alvarez| 123| Cake| 10|19800707| never|\n", + "| 2| Andre| Ampere| 423| piza| 8|19500708| gonna|\n", + "| 3| NiELS| Bohr| 551| pizza| 8|19900709| give|\n", + "| 4| PAUL| dirac| 521| pizza| 8|19540710| you|\n", + "| 5| Albert|Einstein| 634| pizza| 8|19900711| up|\n", + "+---+---------+--------+---------+-------+-----+--------+--------+\n", "only showing top 5 rows\n", "\n" ] @@ -2550,27 +2277,27 @@ "output_type": "stream", "text": [ "Original dataFrame:\n", - "+----+---------+--------+---------+-------+-----+--------+--------+\n", - "| id|firstName|lastName|billingId|product|price| birth|dummyCol|\n", - "+----+---------+--------+---------+-------+-----+--------+--------+\n", - "| 1| Luis| Alvarez| 123| Cake| 10|19800707| never|\n", - "| 2| Andre| Ampere| 423| piza| 8|19500708| gonna|\n", - "| 3| NiELS| Bohr| 551| pizza| 8|19900709| give|\n", - "| 4| PAUL| dirac| 521| pizza| 8|19540710| you|\n", - "|null| Albert|Einstein| 634| pizza| 8|19900711| up|\n", - "+----+---------+--------+---------+-------+-----+--------+--------+\n", + "+---+---------+--------+---------+-------+-----+--------+--------+\n", + "| id|firstName|lastName|billingId|product|price| birth|dummyCol|\n", + "+---+---------+--------+---------+-------+-----+--------+--------+\n", + "| 1| Luis| Alvarez| 123| Cake| 10|19800707| never|\n", + "| 2| Andre| Ampere| 423| piza| 8|19500708| gonna|\n", + "| 3| NiELS| Bohr| 551| pizza| 8|19900709| give|\n", + "| 4| PAUL| dirac| 521| pizza| 8|19540710| you|\n", + "| 5| Albert|Einstein| 634| pizza| 8|19900711| up|\n", + "+---+---------+--------+---------+-------+-----+--------+--------+\n", "only showing top 5 rows\n", "\n", "Dataframe without dummy column:\n", - "+----+---------+--------+---------+-------+-----+--------+\n", - "| id|firstName|lastName|billingId|product|price| birth|\n", - "+----+---------+--------+---------+-------+-----+--------+\n", - "| 1| Luis| Alvarez| 123| Cake| 10|19800707|\n", - "| 2| Andre| Ampere| 423| piza| 8|19500708|\n", - "| 3| NiELS| Bohr| 551| pizza| 8|19900709|\n", - "| 4| PAUL| dirac| 521| pizza| 8|19540710|\n", - "|null| Albert|Einstein| 634| pizza| 8|19900711|\n", - "+----+---------+--------+---------+-------+-----+--------+\n", + "+---+---------+--------+---------+-------+-----+--------+\n", + "| id|firstName|lastName|billingId|product|price| birth|\n", + "+---+---------+--------+---------+-------+-----+--------+\n", + "| 1| Luis| Alvarez| 123| Cake| 10|19800707|\n", + "| 2| Andre| Ampere| 423| piza| 8|19500708|\n", + "| 3| NiELS| Bohr| 551| pizza| 8|19900709|\n", + "| 4| PAUL| dirac| 521| pizza| 8|19540710|\n", + "| 5| Albert|Einstein| 634| pizza| 8|19900711|\n", + "+---+---------+--------+---------+-------+-----+--------+\n", "only showing top 5 rows\n", "\n" ] @@ -2606,27 +2333,27 @@ "output_type": "stream", "text": [ "Original dataFrame:\n", - "+----+---------+--------+---------+-------+-----+--------+\n", - "| id|firstName|lastName|billingId|product|price| birth|\n", - "+----+---------+--------+---------+-------+-----+--------+\n", - "| 1| Luis| Alvarez| 123| Cake| 10|19800707|\n", - "| 2| Andre| Ampere| 423| piza| 8|19500708|\n", - "| 3| NiELS| Bohr| 551| pizza| 8|19900709|\n", - "| 4| PAUL| dirac| 521| pizza| 8|19540710|\n", - "|null| Albert|Einstein| 634| pizza| 8|19900711|\n", - "+----+---------+--------+---------+-------+-----+--------+\n", + "+---+---------+--------+---------+-------+-----+--------+\n", + "| id|firstName|lastName|billingId|product|price| birth|\n", + "+---+---------+--------+---------+-------+-----+--------+\n", + "| 1| Luis| Alvarez| 123| Cake| 10|19800707|\n", + "| 2| Andre| Ampere| 423| piza| 8|19500708|\n", + "| 3| NiELS| Bohr| 551| pizza| 8|19900709|\n", + "| 4| PAUL| dirac| 521| pizza| 8|19540710|\n", + "| 5| Albert|Einstein| 634| pizza| 8|19900711|\n", + "+---+---------+--------+---------+-------+-----+--------+\n", "only showing top 5 rows\n", "\n", "Setting all letters to lowerCase:\n", - "+----+---------+--------+---------+-------+-----+--------+\n", - "| id|firstName|lastName|billingId|product|price| birth|\n", - "+----+---------+--------+---------+-------+-----+--------+\n", - "| 1| luis| alvarez| 123| cake| 10|19800707|\n", - "| 2| andre| ampere| 423| piza| 8|19500708|\n", - "| 3| niels| bohr| 551| pizza| 8|19900709|\n", - "| 4| paul| dirac| 521| pizza| 8|19540710|\n", - "|null| albert|einstein| 634| pizza| 8|19900711|\n", - "+----+---------+--------+---------+-------+-----+--------+\n", + "+---+---------+--------+---------+-------+-----+--------+\n", + "| id|firstName|lastName|billingId|product|price| birth|\n", + "+---+---------+--------+---------+-------+-----+--------+\n", + "| 1| luis| alvarez| 123| cake| 10|19800707|\n", + "| 2| andre| ampere| 423| piza| 8|19500708|\n", + "| 3| niels| bohr| 551| pizza| 8|19900709|\n", + "| 4| paul| dirac| 521| pizza| 8|19540710|\n", + "| 5| albert|einstein| 634| pizza| 8|19900711|\n", + "+---+---------+--------+---------+-------+-----+--------+\n", "only showing top 5 rows\n", "\n" ] @@ -2659,27 +2386,27 @@ "output_type": "stream", "text": [ "Original dataFrame:\n", - "+----+---------+--------+---------+-------+-----+--------+\n", - "| id|firstName|lastName|billingId|product|price| birth|\n", - "+----+---------+--------+---------+-------+-----+--------+\n", - "| 1| luis| alvarez| 123| cake| 10|19800707|\n", - "| 2| andre| ampere| 423| piza| 8|19500708|\n", - "| 3| niels| bohr| 551| pizza| 8|19900709|\n", - "| 4| paul| dirac| 521| pizza| 8|19540710|\n", - "|null| albert|einstein| 634| pizza| 8|19900711|\n", - "+----+---------+--------+---------+-------+-----+--------+\n", + "+---+---------+--------+---------+-------+-----+--------+\n", + "| id|firstName|lastName|billingId|product|price| birth|\n", + "+---+---------+--------+---------+-------+-----+--------+\n", + "| 1| luis| alvarez| 123| cake| 10|19800707|\n", + "| 2| andre| ampere| 423| piza| 8|19500708|\n", + "| 3| niels| bohr| 551| pizza| 8|19900709|\n", + "| 4| paul| dirac| 521| pizza| 8|19540710|\n", + "| 5| albert|einstein| 634| pizza| 8|19900711|\n", + "+---+---------+--------+---------+-------+-----+--------+\n", "only showing top 5 rows\n", "\n", "Dataframe without dummy column:\n", - "+----+---------+--------+---------+-------+-----+----------+\n", - "| id|firstName|lastName|billingId|product|price| birth|\n", - "+----+---------+--------+---------+-------+-----+----------+\n", - "| 1| luis| alvarez| 123| cake| 10|07-07-1980|\n", - "| 2| andre| ampere| 423| piza| 8|08-07-1950|\n", - "| 3| niels| bohr| 551| pizza| 8|09-07-1990|\n", - "| 4| paul| dirac| 521| pizza| 8|10-07-1954|\n", - "|null| albert|einstein| 634| pizza| 8|11-07-1990|\n", - "+----+---------+--------+---------+-------+-----+----------+\n", + "+---+---------+--------+---------+-------+-----+----------+\n", + "| id|firstName|lastName|billingId|product|price| birth|\n", + "+---+---------+--------+---------+-------+-----+----------+\n", + "| 1| luis| alvarez| 123| cake| 10|07-07-1980|\n", + "| 2| andre| ampere| 423| piza| 8|08-07-1950|\n", + "| 3| niels| bohr| 551| pizza| 8|09-07-1990|\n", + "| 4| paul| dirac| 521| pizza| 8|10-07-1954|\n", + "| 5| albert|einstein| 634| pizza| 8|11-07-1990|\n", + "+---+---------+--------+---------+-------+-----+----------+\n", "only showing top 5 rows\n", "\n" ] @@ -2713,27 +2440,27 @@ "output_type": "stream", "text": [ "Original dataFrame:\n", - "+----+---------+--------+---------+-------+-----+----------+\n", - "| id|firstName|lastName|billingId|product|price| birth|\n", - "+----+---------+--------+---------+-------+-----+----------+\n", - "| 1| luis| alvarez| 123| cake| 10|07-07-1980|\n", - "| 2| andre| ampere| 423| piza| 8|08-07-1950|\n", - "| 3| niels| bohr| 551| pizza| 8|09-07-1990|\n", - "| 4| paul| dirac| 521| pizza| 8|10-07-1954|\n", - "|null| albert|einstein| 634| pizza| 8|11-07-1990|\n", - "+----+---------+--------+---------+-------+-----+----------+\n", + "+---+---------+--------+---------+-------+-----+----------+\n", + "| id|firstName|lastName|billingId|product|price| birth|\n", + "+---+---------+--------+---------+-------+-----+----------+\n", + "| 1| luis| alvarez| 123| cake| 10|07-07-1980|\n", + "| 2| andre| ampere| 423| piza| 8|08-07-1950|\n", + "| 3| niels| bohr| 551| pizza| 8|09-07-1990|\n", + "| 4| paul| dirac| 521| pizza| 8|10-07-1954|\n", + "| 5| albert|einstein| 634| pizza| 8|11-07-1990|\n", + "+---+---------+--------+---------+-------+-----+----------+\n", "only showing top 5 rows\n", "\n", "Printing calculation of age born date client\n", - "+----+---------+--------+---------+-------+-----+----------+---------+\n", - "| id|firstName|lastName|billingId|product|price| birth|clientAge|\n", - "+----+---------+--------+---------+-------+-----+----------+---------+\n", - "| 1| luis| alvarez| 123| cake| 10|07-07-1980| 37.7177|\n", - "| 2| andre| ampere| 423| piza| 8|08-07-1950| 67.7124|\n", - "| 3| niels| bohr| 551| pizza| 8|09-07-1990| 27.7151|\n", - "| 4| paul| dirac| 521| pizza| 8|10-07-1954| 63.7258|\n", - "|null| albert|einstein| 634| pizza| 8|11-07-1990| 27.7151|\n", - "+----+---------+--------+---------+-------+-----+----------+---------+\n", + "+---+---------+--------+---------+-------+-----+----------+---------+\n", + "| id|firstName|lastName|billingId|product|price| birth|clientAge|\n", + "+---+---------+--------+---------+-------+-----+----------+---------+\n", + "| 1| luis| alvarez| 123| cake| 10|07-07-1980| 37.7581|\n", + "| 2| andre| ampere| 423| piza| 8|08-07-1950| 67.7527|\n", + "| 3| niels| bohr| 551| pizza| 8|09-07-1990| 27.7554|\n", + "| 4| paul| dirac| 521| pizza| 8|10-07-1954| 63.7661|\n", + "| 5| albert|einstein| 634| pizza| 8|11-07-1990| 27.7554|\n", + "+---+---------+--------+---------+-------+-----+----------+---------+\n", "only showing top 5 rows\n", "\n" ] @@ -2766,27 +2493,27 @@ "output_type": "stream", "text": [ "Original dataframe\n", - "+----+---------+--------+---------+-------+-----+----------+---------+\n", - "| id|firstName|lastName|billingId|product|price| birth|clientAge|\n", - "+----+---------+--------+---------+-------+-----+----------+---------+\n", - "| 1| luis| alvarez| 123| cake| 10|07-07-1980| 37.7177|\n", - "| 2| andre| ampere| 423| piza| 8|08-07-1950| 67.7124|\n", - "| 3| niels| bohr| 551| pizza| 8|09-07-1990| 27.7151|\n", - "| 4| paul| dirac| 521| pizza| 8|10-07-1954| 63.7258|\n", - "|null| albert|einstein| 634| pizza| 8|11-07-1990| 27.7151|\n", - "+----+---------+--------+---------+-------+-----+----------+---------+\n", + "+---+---------+--------+---------+-------+-----+----------+---------+\n", + "| id|firstName|lastName|billingId|product|price| birth|clientAge|\n", + "+---+---------+--------+---------+-------+-----+----------+---------+\n", + "| 1| luis| alvarez| 123| cake| 10|07-07-1980| 37.7581|\n", + "| 2| andre| ampere| 423| piza| 8|08-07-1950| 67.7527|\n", + "| 3| niels| bohr| 551| pizza| 8|09-07-1990| 27.7554|\n", + "| 4| paul| dirac| 521| pizza| 8|10-07-1954| 63.7661|\n", + "| 5| albert|einstein| 634| pizza| 8|11-07-1990| 27.7554|\n", + "+---+---------+--------+---------+-------+-----+----------+---------+\n", "only showing top 5 rows\n", "\n", "Renaming some columns of dataFrame\n", - "+-------+----+---------+--------+---------+-------+-----+----------+\n", - "| age| id|firstName|lastName|billingId|product|price| birth|\n", - "+-------+----+---------+--------+---------+-------+-----+----------+\n", - "|37.7177| 1| luis| alvarez| 123| cake| 10|07-07-1980|\n", - "|67.7124| 2| andre| ampere| 423| piza| 8|08-07-1950|\n", - "|27.7151| 3| niels| bohr| 551| pizza| 8|09-07-1990|\n", - "|63.7258| 4| paul| dirac| 521| pizza| 8|10-07-1954|\n", - "|27.7151|null| albert|einstein| 634| pizza| 8|11-07-1990|\n", - "+-------+----+---------+--------+---------+-------+-----+----------+\n", + "+-------+---+---------+--------+---------+-------+-----+----------+\n", + "| age| id|firstName|lastName|billingId|product|price| birth|\n", + "+-------+---+---------+--------+---------+-------+-----+----------+\n", + "|37.7581| 1| luis| alvarez| 123| cake| 10|07-07-1980|\n", + "|67.7527| 2| andre| ampere| 423| piza| 8|08-07-1950|\n", + "|27.7554| 3| niels| bohr| 551| pizza| 8|09-07-1990|\n", + "|63.7661| 4| paul| dirac| 521| pizza| 8|10-07-1954|\n", + "|27.7554| 5| albert|einstein| 634| pizza| 8|11-07-1990|\n", + "+-------+---+---------+--------+---------+-------+-----+----------+\n", "only showing top 5 rows\n", "\n" ] @@ -2818,27 +2545,27 @@ "output_type": "stream", "text": [ "Original dataframe\n", - "+-------+----+---------+--------+---------+-------+-----+----------+\n", - "| age| id|firstName|lastName|billingId|product|price| birth|\n", - "+-------+----+---------+--------+---------+-------+-----+----------+\n", - "|37.7177| 1| luis| alvarez| 123| cake| 10|07-07-1980|\n", - "|67.7124| 2| andre| ampere| 423| piza| 8|08-07-1950|\n", - "|27.7151| 3| niels| bohr| 551| pizza| 8|09-07-1990|\n", - "|63.7258| 4| paul| dirac| 521| pizza| 8|10-07-1954|\n", - "|27.7151|null| albert|einstein| 634| pizza| 8|11-07-1990|\n", - "+-------+----+---------+--------+---------+-------+-----+----------+\n", + "+-------+---+---------+--------+---------+-------+-----+----------+\n", + "| age| id|firstName|lastName|billingId|product|price| birth|\n", + "+-------+---+---------+--------+---------+-------+-----+----------+\n", + "|37.7581| 1| luis| alvarez| 123| cake| 10|07-07-1980|\n", + "|67.7527| 2| andre| ampere| 423| piza| 8|08-07-1950|\n", + "|27.7554| 3| niels| bohr| 551| pizza| 8|09-07-1990|\n", + "|63.7661| 4| paul| dirac| 521| pizza| 8|10-07-1954|\n", + "|27.7554| 5| albert|einstein| 634| pizza| 8|11-07-1990|\n", + "+-------+---+---------+--------+---------+-------+-----+----------+\n", "only showing top 5 rows\n", "\n", "age column moved\n", - "+----+---------+--------+-------+---------+-------+-----+----------+\n", - "| id|firstName|lastName| age|billingId|product|price| birth|\n", - "+----+---------+--------+-------+---------+-------+-----+----------+\n", - "| 1| luis| alvarez|37.7177| 123| cake| 10|07-07-1980|\n", - "| 2| andre| ampere|67.7124| 423| piza| 8|08-07-1950|\n", - "| 3| niels| bohr|27.7151| 551| pizza| 8|09-07-1990|\n", - "| 4| paul| dirac|63.7258| 521| pizza| 8|10-07-1954|\n", - "|null| albert|einstein|27.7151| 634| pizza| 8|11-07-1990|\n", - "+----+---------+--------+-------+---------+-------+-----+----------+\n", + "+---+---------+--------+-------+---------+-------+-----+----------+\n", + "| id|firstName|lastName| age|billingId|product|price| birth|\n", + "+---+---------+--------+-------+---------+-------+-----+----------+\n", + "| 1| luis| alvarez|37.7581| 123| cake| 10|07-07-1980|\n", + "| 2| andre| ampere|67.7527| 423| piza| 8|08-07-1950|\n", + "| 3| niels| bohr|27.7554| 551| pizza| 8|09-07-1990|\n", + "| 4| paul| dirac|63.7661| 521| pizza| 8|10-07-1954|\n", + "| 5| albert|einstein|27.7554| 634| pizza| 8|11-07-1990|\n", + "+---+---------+--------+-------+---------+-------+-----+----------+\n", "only showing top 5 rows\n", "\n" ] @@ -2875,42 +2602,41 @@ "output_type": "stream", "text": [ "Original dataframe\n", - "+----+---------+--------+-------+---------+-------+-----+----------+\n", - "| id|firstName|lastName| age|billingId|product|price| birth|\n", - "+----+---------+--------+-------+---------+-------+-----+----------+\n", - "| 1| luis| alvarez|37.7177| 123| cake| 10|07-07-1980|\n", - "| 2| andre| ampere|67.7124| 423| piza| 8|08-07-1950|\n", - "| 3| niels| bohr|27.7151| 551| pizza| 8|09-07-1990|\n", - "| 4| paul| dirac|63.7258| 521| pizza| 8|10-07-1954|\n", - "|null| albert|einstein|27.7151| 634| pizza| 8|11-07-1990|\n", - "+----+---------+--------+-------+---------+-------+-----+----------+\n", + "+---+---------+--------+-------+---------+-------+-----+----------+\n", + "| id|firstName|lastName| age|billingId|product|price| birth|\n", + "+---+---------+--------+-------+---------+-------+-----+----------+\n", + "| 1| luis| alvarez|37.7581| 123| cake| 10|07-07-1980|\n", + "| 2| andre| ampere|67.7527| 423| piza| 8|08-07-1950|\n", + "| 3| niels| bohr|27.7554| 551| pizza| 8|09-07-1990|\n", + "| 4| paul| dirac|63.7661| 521| pizza| 8|10-07-1954|\n", + "| 5| albert|einstein|27.7554| 634| pizza| 8|11-07-1990|\n", + "+---+---------+--------+-------+---------+-------+-----+----------+\n", "only showing top 5 rows\n", "\n", " Multiplying by 20 a number if value in cell is greater than 20:\n", - "+----+------------+--------+--------+---------+----------+-----+----------+\n", - "| id| firstName|lastName| age|billingId| product|price| birth|\n", - "+----+------------+--------+--------+---------+----------+-----+----------+\n", - "| 1| luis| alvarez| 37.7177| 123| cake| 200|07-07-1980|\n", - "| 2| andre| ampere| 67.7124| 423| piza| 160|08-07-1950|\n", - "| 3| niels| bohr| 27.7151| 551| pizza| 160|09-07-1990|\n", - "| 4| paul| dirac| 63.7258| 521| pizza| 160|10-07-1954|\n", - "|null| albert|einstein| 27.7151| 634| pizza| 160|11-07-1990|\n", - "| 6| galileo| galilei| 87.7204| 672| arepa| 100|12-08-1930|\n", - "| 7| carl| gauss| 47.7231| 323| taco| 60|13-07-1970|\n", - "|null| david| hilbert| 67.7124| 624| taaaccoo| 60|14-07-1950|\n", - "| 9| johannes| kepler| 97.7231| 735| taco| 60|22-04-1920|\n", - "| 10| james| maxwell| 94.7151| 875| taco| 60|12-03-1923|\n", - "|null| isaac| newton| 18.7258| 992| pasta| 180|15-02-1999|\n", - "| 12| emmy| noether| 24.7258| 234| pasta| 180|08-12-1993|\n", - "| 13| max| planck| 23.7285| 111|hamburguer| 80|04-01-1994|\n", - "| 14| fred| hoyle| 20.7204| 553| pizzza| 160|27-06-1997|\n", - "| 15| heinrich | hertz| 61.7124| 116| pizza| 160|30-11-1956|\n", - "| 16| william| gilbert| 59.7204| 886| beer| 40|26-03-1958|\n", - "| 17| marie| curie| 17.7285| 912| rice| 20|22-03-2000|\n", - "| 18| arthur| compton|118.7124| 812| 110790| 100|01-01-1899|\n", - "| 19| james|chadwick| 96.7285| 467| null| 200|03-05-1921|\n", - "| 19| james|chadwick| 96.7285| 467| null| 200|03-05-1921|\n", - "+----+------------+--------+--------+---------+----------+-----+----------+\n", + "+---+------------+--------+--------+---------+----------+-----+----------+\n", + "| id| firstName|lastName| age|billingId| product|price| birth|\n", + "+---+------------+--------+--------+---------+----------+-----+----------+\n", + "| 1| luis| alvarez| 37.7581| 123| cake| 200|07-07-1980|\n", + "| 2| andre| ampere| 67.7527| 423| piza| 160|08-07-1950|\n", + "| 3| niels| bohr| 27.7554| 551| pizza| 160|09-07-1990|\n", + "| 4| paul| dirac| 63.7661| 521| pizza| 160|10-07-1954|\n", + "| 5| albert|einstein| 27.7554| 634| pizza| 160|11-07-1990|\n", + "| 6| galileo| galilei| 87.7608| 672| arepa| 100|12-08-1930|\n", + "| 7| carl| gauss| 47.7634| 323| taco| 60|13-07-1970|\n", + "| 8| david| hilbert| 67.7527| 624| taaaccoo| 60|14-07-1950|\n", + "| 9| johannes| kepler| 97.7634| 735| taco| 60|22-04-1920|\n", + "| 10| james| maxwell| 94.7554| 875| taco| 60|12-03-1923|\n", + "| 11| isaac| newton| 18.7661| 992| pasta| 180|15-02-1999|\n", + "| 12| emmy| noether| 24.7661| 234| pasta| 180|08-12-1993|\n", + "| 13| max| planck| 23.7688| 111|hamburguer| 80|04-01-1994|\n", + "| 14| fred| hoyle| 20.7608| 553| pizzza| 160|27-06-1997|\n", + "| 15| heinrich | hertz| 61.7527| 116| pizza| 160|30-11-1956|\n", + "| 16| william| gilbert| 59.7608| 886| beer| 40|26-03-1958|\n", + "| 17| marie| curie| 17.7688| 912| rice| 20|22-03-2000|\n", + "| 18| arthur| compton|118.7527| 812| 110790| 100|01-01-1899|\n", + "| 19| james|chadwick| 96.7688| 467| null| 200|03-05-1921|\n", + "+---+------------+--------+--------+---------+----------+-----+----------+\n", "\n" ] } @@ -2953,10 +2679,10 @@ { "data": { "text/html": [ - "
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Column name: firstName
Column datatype: string
DatatypeQuantityPercentage
None00.00 %
Empty str00.00 %
String19100.00 %
Integer00.00 %
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t6x6zyk/7aAr19V0fO8+kMaw0DyvVYgXMw5ed5xHu61lj6mhsz+HifDOxY992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\n9cU8HWBEu3Yd9Oaba+rdZ82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iCgYAAID9RAX6DfPmzdPOnTu1YsUKHTx4UFOnTtXll1+u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\nffv2SZ6nq6sL3759w+bNmyXvW278/f1x9uxZHDp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ve2+23r37qlGjxqbHLBK7ZySftfNJ9s9IPmvnk+yf0e75igt7Sv8Ee0qv77PP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\n7Jjee+890yO6zKlTp/Tjjz8qJydH0pU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uZLNrRvJZn90z2j2fZP+M5LO+2pCxIspdSn8pKSlJ6enpWr16dZnH8vLy5Ofn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\nDnd3d9FRzC4mJgZZWVkICgqCRqMx2Yah0WgEJWu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q6MCBA1f9fmFhYVlO1btcp5WWZucGvCA9PYOMDkc+57M9o+35JPszks/5CkNG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\npaenq2nTpnrooYes+F2fW7jRybBp06b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vURuew+LgmlIfoJRemtvt1tq1a7Rr106lpR2V252pgAB/BQeHqFWrUN1++x2q\nUqVq/v5O/Eb0JCP5yh5eozyHZZ3t+SReozY8h56ilPoApdS7bP9GJJ/z2Z7R9nyS/RnJ53zlIWNx\nSinrlKJEEhJ2aenSJRddm61XrwfVokVL02OWiO0ZyefsfJL9Gcnn7HyS/Rltz1daOFJ6GRwpvbhP\nPvlYr78+Xnff3V1hYW0Lrc22Y8c2rV27RjExY3THHXdJct6/Dj3NSL6yhdcoz2FZz2h7PonXqA3P\nYXFw+t4HKKUX9+CD9+nRR/+sHj3uu+g+q1Yt17vvvq3Fi5dLct43oqcZyVe28Bo9j+ew7LI9n8Rr\nVHL+c1gcrFOKUnXixAm1bh12yX1atmyttLSjpTSR99mekXzOzifZn5F8zs4n2Z/R9nyliVKKYrvx\nxo6aMWOqUlJ+ueDHjx5N1YwZU3XjjTeV8mTeY3tG8jk7n2R/RvI5O59kf0bb85UmTt9fBqfvL+7U\nqZMaP36sNm36UnXr1vvd2mxpSkk5rI4dO2n06Nd0xRVXSHLeKQtPM5KvbOE1ynNY1jPank/iNWrD\nc1gcXFPqA5TSy/v554O/WZvNLX//AIWEnF+b7aqr6hfY16nfiEXNSL6yidcoz2FZZ3s+idfobzk1\noydYEgpG1K/fQPXrN5AkHTmSotq1g1WxYkXDU3mX7RnJ53y2ZySf89me0fZ8pYFrSuFV/fs/qF9+\nOWx6DJ+yPSP5nM/2jORzPtsz2p7PVyil8CqHXQ1SLLZnJJ/z2Z6RfM5ne0bb8/kKp+9RYgsWzMv/\nfU7OOX3wwWIFBQVJkh5//ClTY3mV7RnJ53y2ZySf89me0fZ8pYFSihI7fPhQ/u9zc3OVmpqis2fP\nGJzI+2zPSD7nsz0j+ZzP9oy25ysN3H1/Gdx975m77rpFb7+9KP9i79+z4Y7DS2UkX9nHa9TufJLz\nM9qeT+I1akPGy+EnOgEAAMCRKo4dO3as6SE8kZ6eVaqPN2/TgVJ9vKF3Xiu3O1u5uY46gJ2vXr0r\n1aJFS1WqVOmCH69QwaXKlf2tzUi+so/XqN35JOdntD2fxGvUhoyXU7VqgMefw5FSeFV4eBf99NMB\nZWVlWXstje0Zyed8tmckn/PZntH2fL7CjU7wiszMTE2fPkWrV6+UJC1a9KHefHOG3G63xo6Nzb8D\n0clsz0g+Z+eT7M9IPmfnk+zPaHs+X+NIKbxi1qyZ2rcvWfPnvyd///OH7J944mmdPHlCM2ZMMTyd\nd9iekXwFmtJxAAAgAElEQVTOZ3tG8jmf7Rltz+drlFJ4xYYN6zV06Atq1qx5/rZmzZorOvolffXV\nfwxO5j22ZySf89mekXzOZ3tG2/P5GqUUXpGeflYBAYGFtufl5SonJ8fARN5ne0byOZ/tGcnnfLZn\ntD2fr1FK4RXh4bdo7ty3lJ5+VpLkcrl06NDPmjZtijp3Djc8nXfYnpF8zmd7RvI5n+0Zbc/nayye\nfxksnl80Z86c0cSJr+rLLz9Xbm6uqlWrrrNnz6hjx0565ZVxCgqqIcnZCwYXJSP5yi5eozyHZZ3t\n+SReozY8h0VVnMXzKaWXQSn1zM8/H9SBA/uVk3NOjRo11tVXNy7wcRu+ES+VkXxlH69Ru/NJzs9o\nez6J16gNGS+nOKWUJaFQYr/8clg//PC9jhw5ouzsLAUGBqp27WAFBHi+cG5ZZXtG8jmf7RnJ53y2\nZ7Q9X2mglKLYTp48odjYV/XVVxtVt2491axZS/7+/srKytKxY2lKTT2im2/uopiYVxy7NpvtGcnn\n7HyS/RnJ5+x8kv0Zbc9XmiilKLZJk2KVkZGuDz5YqTp16hb6eErKL4qNHavJk2M1fvwkAxOWnO0Z\nyefsfJL9Gcnn7HyS/Rltz1eauPsexbZ58yY9//yIC34TSlLduvUUFTVcmzd/VcqTeY/tGcnn7HyS\n/RnJ5+x8kv0Zbc9XmiilKLbatYOVmLj3kvskJOxS9eqeX+xcVtiekXzOzifZn5F8zs4n2Z/R9nyl\nidP3KLYnn3xGkyaN17ffblbbtjcoODhElSpVUnZ2ttLSjmrHju1as2a1RoyIMT1qsdmekXzOzifZ\nn5F8zs4n2Z/R9nyliSWhLoMloS5t166dWrp0iX744XulpaUpM9Mtf39/BQeHqFWrUPXs+YBatw7N\n39+Jy2B4kpF8ZQ+vUZ7Dss72fBKvURueQ0+xTqkPUEq9y/ZvRPI5n+0Zbc8n2Z+RfM5XHjKyTilK\nXWamW+vWrb3g2mytWoUqIuLOC/4cYCexPSP5nJ1Psj8j+ZydT7I/o+35SgtHSi+DI6UXt3t3gqKj\nh6hy5aoKC2tTaG2277/fLrfbralTZ6p582skOe9fh55mJF/ZwmuU57CsZ7Q9n8Rr1IbnsDg4fe8D\nlNKLe+qpAWrdOkxDhgy/6D7Tp0/Vf//7g+bMWSDJed+InmYkX9nCa/Q8nsOyy/Z8Eq9RyfnPYXEU\np5SyJBSKbd++JN1/f+9L7tOzZ28lJV16qYyyzPaM5HN2Psn+jORzdj7J/oy25ytNlFIUW9OmzbVq\n1YpL7rNixVI1atS4dAbyAdszks/Z+ST7M5LP2fkk+zPanq80cfr+Mjh9f3F79iRoxIihCgwMVFhY\n20Jrs+3cuUNnzpzR5MnT1KLF9ZKcd8rC04zkK1t4jfIclvWMtueTeI3a8BwWB9eU+gCl9NLcbrfW\nrl2jXbt2Ki3tqNzuTAUE/G9ttttvv0NVqlTN39+J34ieZCRf2cNrlOewrLM9n8Rr1Ibn0FOUUh+g\nlHqX7d+I5HM+2zPank+yPyP5nK88ZORGJ5Q5mZmZ+vjjVabH8CnbM5LP+WzPSD7nsz2j7fm8hVIK\nnzp79owmTHjV9Bg+ZXtG8jmf7RnJ53y2Z7Q9n7cUu5RmZWWpR48e+vrrry+6z65du9SnTx+1adNG\nvXv31s6dO4v7cHCoWrVq64svvjE9hk/ZnpF8zmd7RvI5n+0Zbc/nLcUqpZmZmRo2bJj27r34mlvp\n6emKjIxUhw4dtHTpUrVr105PP/200tPTiz0sAAAA7OTn6SckJiZq+PDhutz9UatXr1ZAQICio6Pl\ncrn00ksv6fPPP9e//vUv9erVq9gDo+zYtm1rkfdt2/YGH07iO7ZnJN//ODGfZH9G8v2PE/NJ9me0\nPV9p8riUbt68WTfddJOef/55tW3b9qL7bd++Xe3bt5fL5ZIkuVwu3XDDDdq2bRul1BJvvDFJ+/fv\nk6RL/iPF5XLp8883l9ZYXmV7RvKd59R8kv0ZyXeeU/NJ9me0PV9p8riU9uvXr0j7paamqnnz5gW2\n1a5d+5Kn/OEs8fHvauzYl3T48M+aPXuBAgICTI/kdbZnJJ/z2Z6RfM5ne0bb85WmEq1Tet111+md\nd97RTTfdVOhjAwYMUPv27RUVFZW/bcaMGfruu+/09ttvF+nrHzlyRKmpqQW2+flVUZ06dYo7ssfa\nTfp3qT2WdH6d0lOnMpST44x1y7KysvTkkwPUoUNHRUU9f9n9K1asoKCgytZmJF/Zw2u0INvzSc7L\naHs+idfo7zkxo6dq1qx6+Z1+x2elNDIyUtdee61eeOGF/G1TpkxRUlKSZs+eXaSv/9e//lVxcXEF\ntg0ePLhA0fW1xiM/KrXHks6X0tJW0oyuUylypSUpt8nNRdq/tDN64zn0JCP5vI/X6OWV5eewtPNJ\nzstoez6J1+jvmXgvLes8Pn1fVHXr1tXRo0cLbDt69KhHRzn79u2riIiIAtv8/Kro+PGzXpmxrHLa\nv5zyguoqL6iuR59je0bylS28RguzPZ/krIy255N4jV6I0zJ6ojhHSn1WStu0aaN58+YpLy9PLpdL\neXl52rp1q5555pkif406deoUKrGpqaet/ZFcv8rJySWjw5HP+WzPaHs+yf6M5HO+8pDRE179iU6p\nqalyu92SpG7duunUqVOKjY1VYmKiYmNjlZGRoe7du3vzIQEAAGABr5bS8PBwrV69WpJUrVo1zZkz\nR1u2bFGvXr20fft2zZ07V1WqVPHmQwIAAMACJTp9v3v37kv+OSwsTMuWLSvJQwAAAKAc8OqRUgAA\nAKA4KKUAAAAwjlIKAAAA4yilAAAAMI5SCgAAAOMopQAAADCOUgoAAADjKKUAAAAwjlIKAAAA4yil\nAAAAMI5SCgAAAOMopQAAADCOUgoAAADjKKUAAAAwjlIKAAAA4yilAAAAMI5SCgAAAOMopQAAADCO\nUgoAAADjKKUAAAAwjlIKAAAA4yilAAAAMI5SCgAAAOMopQAAADCOUgoAAADjKKUAAAAwjlIKAAAA\n4yilAAAAMI5SCgAAAOMopQAAADCOUgoAAADjKKUAAAAwjlIKAAAA4yilAAAAMI5SCgAAAOMopQAA\nADCOUgoAAADjKKUAAAAwjlIKAAAA4yilAAAAMI5SCgAAAOMopQAAADCOUgoAAADjKKUAAAAwjlIK\nAAAA4yilAAAAMI5SCgAAAOMopQAAADCOUgoAAADjKKUAAAAwjlIKAAAA4yilAAAAMI5SCgAAAOMo\npQAAADCOUgoAAADjKKUAAAAwjlIKAAAA4yilAAAAMI5SCgAAAOMopQAAADCOUgoAAADjKKUAAAAw\njlIKAAAA4yilAAAAMI5SCgAAAOMopQAAADCOUgoAAADjKKUAAAAwjlIKAAAA4yilAAAAMM7jUpqZ\nmalRo0apQ4cOCg8P1/z58y+678CBA3XdddcV+LV+/foSDQwAAAD7+Hn6CZMnT9bOnTu1cOFCHTp0\nSC+++KKuuuoqdevWrdC+SUlJmjJlijp37py/rUaNGiWbGAAAANbxqJSmp6dryZIlmjdvnlq1aqVW\nrVpp7969eu+99wqV0qysLB08eFChoaEKCQnx6tAAAACwi0en7xMSEnTu3Dm1a9cuf1v79u21fft2\n5ebmFtg3OTlZLpdLDRs29M6kAAAAsJZHR0pTU1NVs2ZN+fv7528LDg5WZmamTpw4oVq1auVvT05O\nVrVq1RQdHa3NmzerXr16eu6553TrrbcW+fGOHDmi1NTUggP7VVGdOnU8GdtxKla0//4z2zOSz/ls\nz2h7Psn+jORzvvKQ0RMeldKMjIwChVRS/p+zsrIKbE9OTpbb7VZ4eLgiIyP16aefauDAgVq8eLFC\nQ0OL9HiLFy9WXFxcgW2DBw9WVFSUJ2M7TlBQZdMj+JztGcnnfLZntD2fZH9G8jlfecjoCY9KaUBA\nQKHy+eufAwMDC2wfNGiQHnnkkfwbm1q0aKEffvhB//znP4tcSvv27auIiIiCA/tV0fHjZz0Z23FO\nncpQTk7u5Xd0MNszks/5bM9oez7J/ozkcz6bM9asWdXjz/GolNatW1fHjx/XuXPn5Od3/lNTU1MV\nGBiooKCgAvtWqFCh0J32TZs2VWJiYpEfr06dOoVO1aemnta5c3Y+gb/Kycklo8ORz/lsz2h7Psn+\njORzvvKQ0RMeXczQsmVL+fn5adu2bfnbtmzZotDQUFWoUPBLjRw5UjExMQW2JSQkqGnTpiUYFwAA\nADbyqJRWrlxZPXv21NixY7Vjxw6tXbtW8+fP16OPPirp/FFTt9stSYqIiNDKlSu1fPlyHThwQHFx\ncdqyZYv69+/v/RQAAABwNI9v+4qJiVGrVq00YMAAvfrqq3ruuefUtWtXSVJ4eLhWr14tSeratavG\njBmjWbNmqUePHlq3bp3i4+PVoEED7yYAAACA43n8E50qV66sSZMmadKkSYU+tnv37gJ/7tOnj/r0\n6VP86QAAAFAusEAWAAAAjKOUAgAAwDhKKQAAAIyjlAIAAMA4SikAAACMo5QCAADAOEopAAAAjKOU\nAgAAwDhKKQAAAIyjlAIAAMA4SikAAACMo5QCAADAOEopAAAAjKOUAgAAwDhKKQAAAIyjlAIAAMA4\nSikAAACMo5QCAADAOEopAAAAjKOUAgAAwDhKKQAAAIyjlAIAAMA4SikAAACMo5QCAADAOEopAAAA\njKOUAgAAwDhKKQAAAIyjlAIAAMA4SikAAACMo5QCAADAOEopAAAAjKOUAgAAwDhKKQAAAIyjlAIA\nAMA4SikAAACMo5QCAADAOEopAAAAjKOUAgAAwDhKKQAAAIyjlAIAAMA4SikAAACMo5QCAADAOEop\nAAAAjKOUAgAAwDhKKQAAAIyjlAIAAMA4SikAAACMo5QCAADAOEopAAAAjKOUAgAAwDhKKQAAAIyj\nlAIAAMA4SikAAACMo5QCAADAOEopAAAAjKOUAgAAwDhKKQAAAIyjlAIAAMA4SikAAACMo5QCAADA\nOEopAAAAjKOUAgAAwDhKKQAAAIyjlAIAAMA4SikAAACMo5QCAADAOEopAAAAjKOUAgAAwDhKKQAA\nAIzzuJRmZmZq1KhR6tChg8LDwzV//vyL7rtr1y716dNHbdq0Ue/evbVz584SDQsAAAA7eVxKJ0+e\nrJ07d2rhwoUaM2aM4uLi9K9//avQfunp6YqMjFSHDh20dOlStWvXTk8//bTS09O9MjgAAADs4VEp\nTU9P15IlS/TSSy+pVatWuuuuu/Tkk0/qvffeK7Tv6tWrFRAQoOjoaDVr1kwvvfSSqlatesECCwAA\ngPLNo1KakJCgc+fOqV27dvnb2rdvr+3btys3N7fAvtu3b1f79u3lcrkkSS6XSzfccIO2bdvmhbEB\nAABgEz9Pdk5NTVXNmjXl7++fvy04OFiZmZk6ceKEatWqVWDf5s2bF/j82rVra+/evUV+vCNHjig1\nNbXgwH5VVKdOHU/GdpyKFe2//8z2jORzPtsz2p5Psj8j+ZyvPGT0SJ4Hli1blnfbbbcV2Pbjjz/m\nXXvttXmHDx8usP3RRx/NmzFjRoFt06dPzxswYECRH2/mzJl51157bYFfM2fO9GRkR0lJScmbOXNm\nXkpKiulRfMb2jORzPtsz2p4vL8/+jORzvvKQsTg8qugBAQHKysoqsO3XPwcGBhZp39/vdyl9+/bV\n0qVLC/zq27evJyM7SmpqquLi4godHbaJ7RnJ53y2Z7Q9n2R/RvI5X3nIWBwenb6vW7eujh8/rnPn\nzsnP7/ynpqamKjAwUEFBQYX2PXr0aIFtR48e9ejUe506daw/VQ8AAAAPb3Rq2bKl/Pz8CtystGXL\nFoWGhqpChYJfqk2bNvruu++Ul5cnScrLy9PWrVvVpk0bL4wNAAAAm3hUSitXrqyePXtq7Nix2rFj\nh9auXav58+fr0UcflXT+qKnb7ZYkdevWTadOnVJsbKwSExMVGxurjIwMde/e3fspAAAA4GgVx44d\nO9aTT+jUqZN27dqlv/zlL9q0aZOeeeYZ9e7dW5J0ww036Oqrr1bLli3l7++vjh07atGiRZo9e7bO\nnTunN954Q1dddZUvclijatWq6tixo6pWrWp6FJ+xPSP5nM/2jLbnk+zPSD7nKw8ZPeXK+/X8OgAA\nAGAIC2QBAADAOEopAAAAjKOUAgAAwDhKKQAAAIyjlAIAAMA4SikAAACMo5QCAADAOEopAAAAjKOU\nAgAAwDhKKQAAAIyjlBq2cePGC24/dOiQBg0aVMrTeN+jjz6qU6dOFdp+7Ngx9erVy8BE8NTy5ct1\n5syZQtvPnDmjmJgYAxN5X69evbR7927TY/jc3r179emnnyo9PV0//fSTbPkp0+Xhfebbb79VVlaW\n6TF86tSpU8rMzJQkJSQkKD4+Xps2bTI8FUqTn+kByrtBgwZp8uTJuvvuuyVJ2dnZmjdvnubOnavr\nrrvO8HTF8/nnn2vHjh2SpG+++UazZ89WlSpVCuxz4MAB/fzzzybG87qff/5Z06dP1/fff69z584V\n+ov+s88+MzSZd4wcOVJXX321ZsyYoRYtWuRvd7vdWr58uSZOnGhwOu84cuSIKlasaHoMnzl58qSG\nDBmizZs3S5LWrFmj2NhY/fTTT5o7d67q169veELPlbf3mcGDB2vhwoUFvgdtsnbtWr3wwgt66623\nVL9+fT388MOqV6+e3nzzTQ0fPlz9+/c3PWKJ7Nq1S+PHj8//e+L3/vvf/xqYquyhlBr2l7/8RSNG\njNCpU6dUr149jRs3Tunp6RozZozuv/9+0+MVS5MmTRQfH6+8vDzl5eVp69atqlSpUv7HXS6XqlSp\notjYWINTek90dLSOHz+uhx9+WNWqVTM9jk/84Q9/0P/93/9p1KhR6tOnj+lxvK5nz5568sknde+9\n96p+/foKCAgo9HEnGz9+vCpXrqyvvvpKt956qyRpwoQJGjFihMaPH69Zs2YZntBz5e195pprrtGO\nHTusLaXTp09XVFSUbr75Zk2dOlVXXnmlVq1apfXr12vcuHGOL6WjRo1S9erVNWPGDGv/nvAGV54t\n528cbOvWrRo4cKBOnz6txx9/XAMHDrTmRfvCCy9o7Nix1uS5kNDQUC1btkzNmzc3PYpPtGzZUl9+\n+aV27typ6Oho3XbbbXrttdd05swZhYeHW/Ev/IiIiIt+zOVyOf5od6dOnfTuu+/qmmuuUbt27bRi\nxQo1bNhQiYmJeuihh/Ttt9+aHrFEysP7zODBg7Vu3TrVrl1bDRo0kL+/f4GPv/POO4Ym846wsDCt\nWbNGV155pbp3765u3bppyJAhOnjwoHr06KFt27aZHrFEwsLCtHLlSl199dWmRynTOFJqwDfffFNo\n2/PPP6/Y2FidO3dOCQkJ+aeAb7zxxtIez6s2bdqk/fv3q3Xr1qZH8ZnGjRvr2LFjpsfwuVtvvVUf\nfvihoqKi1Lt3b2uOQEnSunXrTI/gc79eq/dbx44dk5+f8/8aKA/vMy1btlTLli1Nj+EzV111lb7+\n+mvVrVtX+/bty/+H4sqVK9W4cWOzw3lBy5YtlZSURCm9DOe/GznQI488ctGPLVy4UAsXLpR0/giN\n049CBQcHKy0tzfQYPvXUU09p9OjRevzxx3X11VcXOIUoOf8fFr89mdKgQQO9//77evXVV/XYY4+Z\nG8oHzpw5o+TkZGVlZRXI7HK51KFDB4OTlVyPHj0UGxur1157TS6XS+np6frqq680ZswY/fGPfzQ9\nXomVh/eZZ5991vQIPhUVFaXo6Gjl5OTotttuU2hoqCZNmqT3339fcXFxpscrsfvuu0+jR49Wr169\nLvj3hNMvEfIWTt/Dp2JiYrRixQqFhoaqfv36hU452XCTzKWu8bLhHxZxcXF64oknVLly5QLblyxZ\nohUrVujdd981NJn3rFq1SqNGjbrg3c02PIdZWVl644039N577yk7O1sul0sVK1bUAw88oJEjRyow\nMND0iCVSHt5nJGnFihV6++239eOPP2rZsmV65513FBISosjISNOjecWxY8eUkpKSf0Q4OTlZQUFB\nCg4ONjxZydl+iZC3UEoNy8rK0vTp0/PvNpTOL09z8803a8iQIYX+NeU0l1syyJa/LMqDvXv3av/+\n/frDH/6gtLQ0NWjQQC6Xy/RYXnH77bere/fuGjRokJXXJX777bcKDQ1VXl6efvrpJ+Xk5Khhw4aq\nWrWq6dG8ojy8zyxatEhvvfWWnnnmGU2ZMkWrVq3S1q1bNWHCBD3yyCNWHElNSkpSnTp1VL16dX3x\nxRdat26drr/+eitvrsSFUUoNe+WVV7Rlyxa99tprat++vaTzS2NMnz5dnTp10ujRow1PiKJwu91a\nsWKFkpKSlJOTo6ZNm+qPf/yjrrjiCtOjldillhOaN2+errrqKsMTllzbtm21atUqNWjQwPQoPnHT\nTTdZvZxQedC9e3e9+OKLuu222wrcrLZhwwa98sor2rBhg+kRS2Tx4sV67bXXtGDBAlWrVk0PPvig\nOnXqpISEBPXp00dDhgwxPaLHvvnmG7Vr105+fn4XvJfkVzZcIuQtLJ5v2CeffKKpU6fmF1JJuvPO\nOzVx4kStXr3a4GTes2XLFkVFRem+++7T4cOHNXfuXH300Uemx/KaPXv2qGvXrpo1a5YOHTqkQ4cO\nac6cOerevbsSExNNj1div11O6NelkiZMmJC/hJkNIiIi9Omnn5oew2d+XU7IZra/zxw6dEjNmjUr\ntL1hw4Y6ceKEgYm8Kz4+XpMmTVLHjh314YcfqmXLloqPj9e0adO0ZMkS0+MVyyOPPKKTJ0/m//5S\nv3AeNzoZlpeXd8G7YvPy8pSdnW1gIu/65JNPFBMTowcffFD//ve/de7cOfn5+WnkyJE6efKk+vXr\nZ3rEEouNjdUf/vAHjRs3Lv9O5nPnzmn06NGaMGGC5s+fb3jCkvniiy/07rvvKigoKH9brVq1FBMT\no4ceesjgZCXz21O+2dnZmjx5sj755BM1atRIFSoU/Pe600//1qhRQ2PGjNHMmTOtXE6oPLzPtGnT\nRsuXL9dzzz2Xvy0vL0/z589XWFiYwcm8IyUlJf/gzPr169W3b19JUr169XT27FmToxVbQkLCBX+P\ni6OUGnb33Xfr5Zdf1pgxY3T99ddLOv/iHT9+vO666y7D05VcXFycxo4dqz/96U96//33JUl//vOf\nFRISopkzZ1rxl8W2bds0ZsyYAkvr+Pn56amnntIDDzxgcDLvsXk5IUmqVq2a1Xe/2r6cUHl4nxk9\nerQiIyP173//W1lZWXr11Ve1f/9+ud1uzZs3z/R4Jda0aVOtXLlStWrV0qFDh3TnnXcqOztb8+fP\nt+ayk5ycHH3xxRfav3+/evXqpX379qlp06aqXr266dHKDDv+RnGwmJgYvfTSSxowYIByc3MlSRUq\nVFDPnj01atQow9OV3IEDB9S2bdtC28PCwpSSkmJgIu8LCQnRjz/+qKZNmxbY/uOPP1pxI4mtywk5\n/einJ2y4CeZSysP7zLXXXqs1a9ZoxYoVSk5OVk5Oju644w7de++9VrzPvPjiixo6dGj+ke1mzZrp\ntdde06effqrZs2ebHq/EDh8+rD//+c86efKkTp48qTvuuEPx8fH67rvvFB8fb03xLiludCojTp06\npQMHDqhSpUpq0KCBNXcA9+7dW71791a/fv0KXJw/ffp0ff7551q6dKnpEUssPj5eb7/9toYMGZJ/\nGm379u2aOXOmYy/Q/y3blxP61dq1axUfH5//F36TJk3Uv39/a46g2rycUHl4nykPcnNzdfr0adWo\nUUOSdPToUdWoUcPxq9BI0sCBAxUcHKyxY8eqQ4cOWrFiherVq6eXXnpJhw8ftmJpPW/gSKkBl7oj\nz+12F1gT0ekLr8fExOiZZ57RV199pezsbM2ePVsHDhzQzp07Hfnzti/kiSeeUEZGhqZOnZp/UXtw\ncLAee+wx/fnPfzY8Xcnt2LFDzz//vIYOHWrlckKS9P7772vSpEnq37+/IiMjlZubq61bt+rVV19V\ndna245ek+f1yQpLUunVrTZgwQVlZWY4/kloe3mcOHz6sqVOnKiEhQZmZmfr98SSnr3N5qbvTJef/\nXfjtt9/qn//8pypWrJi/rVKlSho0aJDuv/9+g5OVLRwpNaBFixbauHGjateubf3C65KUmpqqRYsW\n5S+X1KRJE/Xr18+KpYSk83fF1qtXTxUqVFBaWpoCAgJUrVo15eTkKCEhQa1atTI9YomUh+WE7rzz\nTj377LOFjoouW7ZMs2fP1po1awxN5h22Lyck2f8+8+ud3A888MAFr0F0erG52PuLv7+/QkJCHF+6\nu3TpomnTpqlDhw4FvgfXr1+vV155RV988YXpEcsEjpQaUN7uyAsJCdGQIUN0+vRpVapUyZrTvb+6\n4447tHHjRtWqVUu1a9fO337w4EH169dP27dvNzhdyf26nJDNpTQtLe2C1yS2a9dOhw8fNjCRd9m+\nnJBk//vM9u3b9eGHH+qaa64xPYpP/P7vwpycHP34448aN26c/vSnPxmaynseeughvfLKK4qOjpYk\n7du3T5s3b9a0adMcfybGmyilht1+++3q0qWLunTpos6dO1tzLemvsrOzNWfOHL3//vv5P5u6Xr16\neuyxxzRgwADD0xXfkiVL8i++z8vLU+/evQstI3Tq1KkLFgGnsX05Ien83enLly/X0KFDC2xftmyZ\nmjdvbmgq77F9OSFb32d+6+qrr86/PKg8qFixopo0aaKRI0cqMjLS8UeCBw8erKCgII0dO1YZGRmK\njIxU7dq19dhjj+mJJ54wPV6Zwel7w7788kv95z//0aZNm7R37161adMmv6Q6/bSvpPzTEkOGDNH1\n11+v3Nxc7dixQzNnzlSvXr00bNgw0yMWS3Z2tj766CPl5uZq1KhRGjVqVIFTai6XS5UrV1anTp3y\nL6XLBE4AABSwSURBVNp3qri4uEt+3OnXI0rSd999p8cee0zXX3+92rRpI+n8Ul///e9/NWfOHHXq\n1MnwhCWzZ8+e/L8EExIS1Llz5wLLCTl9uShb32d+e53lN998ow8++EADBw5Uw4YNC1ybKDn/msuL\n2bRpkwYPHqytW7eaHqVEfnuZV3p6unJyclS9enVrLvPyFkppGXL8+HF99dVXWrNmjT755BPVqlVL\nX375pemxSqR9+/aaM2dOoR+htnHjRg0bNkxff/21ocm8Z9OmTerQoYMVd4iWZ0lJSVqyZImSk5MV\nEBCgJk2a6P/+7/905ZVXmh7NKzIzM7Vy5coC11zaspyQre8zRb1kxob7D377wyx+dfbsWf3nP/9R\n165dNWHCBANTeU/Lli3zL/P6rQMHDujee+91/GVe3sLp+zIgJSVFW7du1ZYtW7R161bt2bNHjRs3\ntuJn4VarVu2CC6xXr17dmoXXX3zxRZ05c0YdO3ZUly5ddMstt6hhw4amx/KajIwMLV68WImJicrJ\nycnfnpWVpV27dunjjz82OJ13ZGdn6+OPP9aqVat09OhRSfp/7d17MNXpHwfwN90vWkpZujHWKIuc\n1Ek63WiblcGGLlvYtp2dtKt7SUs3Cpu2WF3UyK5qV5Nu2C17pnZLxXKQkghZl1AtKfkVuXx/fzTO\nEJuKes73ez6vGTPNc/zxZvKc53ye5/t5oK2tjUGDBglm+7dXr14wMzODmpoaVFVVYWhoKIgFKSDc\neUYZnjl4FXV1daxfvx4ODg6so7wVZTrm1VWoUsqYlZUV7t27B1NTU4hEIpibm2Ps2LFtPk3xSVlZ\nmfzf586dw/Hjx+Ht7Q0TExN069YNubm58PX1xYIFC3h9TWVLeXl5SEpKQmJiImQyGTQ1NeXHMKZO\nnco6XqesWbMGiYmJsLS0RHx8PGxsbFBUVITMzEx4eHgIYvteqNu/zSoqKrBs2TJkZGRgwIABaGpq\nQk1NDSZNmoTdu3fz8kYZZZtnrK2tcfLkSairq7cav3//Pj777DMkJSUxSkb+izId8+oqtChlzMfH\nBzKZDI8ePcLYsWNhbm6OcePGwdjYmLef8EeNGgUVFRUAaNVL7+UxIWw5tScrKwsRERE4e/YsAPD+\nZxw/fjxCQkJgaWkJe3t7+Pv7w9jYGIGBgSgvL0dISAjriJ0m1O3fZkuWLMGzZ8/g7++PYcOGAXix\nbejt7Q0tLS388MMPjBO+OWWYZ+Lj4+Xtuk6fPo1Zs2ahV69erb6ntLQUBQUFvD/qxXEcLly4gLy8\nvHZ3ZMLDwxmm67yUlBSMHTuWt+/r7wv9dhjbtm0bgBeVDJlMhtTUVPj6+qKwsBAff/wxL2954Hs/\nuTeVkpKC9PR0pKenIyMjAyoqKhCJRFi9erUgjmDU1dVBV1cXwIv2UDdv3oSxsTHmzZsHFxcXtuG6\niFC3f5ulpKTg+PHj8gUp8OJpbh8fH97eC68M84xYLG7VQ7a9GpKBgQHWrl37PmO9E35+fjhx4gSM\njIxw48YNiEQiFBcXo6KiAp9//jnreJ1mZGSE4OBgODo6QldXF15eXpBKpTAyMkJQUBCGDh3KOqJC\n4P9sKxC9e/dG37590bNnT6iqqqKhoaHdCYgPlO2Py83NDaqqqpgyZQpCQ0MhFovl1Roh0NfXR2Ji\nIpydnWFgYIC0tDTMnz8fT548QV1dHet4b63l9q+bmxvWr1/f7vZvyzZKfDV8+HDcvn27TY/LsrIy\n3jaXV4Z5ZuDAgQgICADw4uddvHgx+vbtyzjVu3H27Fns3LkTM2fOxKeffootW7bIW0LV19ezjtdp\nW7duRU5ODpycnBAXFwepVAp/f3/Ex8dj69atOHjwIOuICoG27xnbsWMHUlJSkJOTAx0dHVhaWkIi\nkcDCwoK3PUtbbqt1hK/bai2lpaUhNTUVMpkMGRkZ0NLSgrm5ufyrZXWKjy5cuIAVK1Zg06ZNmDx5\nMmxtbSEWi3H79m2YmZlh9+7drCO+FWXY/m0WGRmJPXv2wNnZWX7FcXZ2Ng4fPgxHR0cYGhrKv/fl\nW60UlbLNMwDw5MkTxMbGorCwEEuXLsX169ehr6+PESNGsI7WacbGxpBKpdDR0cHy5csxdepUODk5\nIS8vD1999RUSEhJYR+wUsViMw4cPY9SoUfjmm2/Qq1cv7N69G4WFhZg9ezauXbvGOqJCoEopY4WF\nhXB0dIREIhHExAK8eAMUUqWwI82LzyVLlqCpqQlZWVmIjo6Gj48PGhoaeP+GaG1tjXPnzqGpqQna\n2tr49ddfERMTg7Fjx8LV1ZV1vLemDNu/zSIjI6GmpoY//vij1ZWp/fr1azWmoqLCm0Wpss0zubm5\n+OKLL6CtrY3c3Fy4ublBKpUiPj4eBw4cgFgsZh2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7775R69Zt1LdvP23YsEFLlizV8ePHdN11EfrTnx7QH//Y3+yYANyYQ0vosWPHlJCQoG++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\nBKWlpTrFfBulsLBQmzZt1J4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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3069,16 +2833,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "end of __analyze 1.6263179779052734\n" + "end of __analyze 4.863485097885132\n" ] }, { "data": { "text/html": [ - "
Column name: billingId
Column datatype: int
DatatypeQuantityPercentage
None00.00 %
Empty str00.00 %
String00.00 %
Integer20100.00 %
Float00.00 %
" + "
Column name: billingId
Column datatype: int
DatatypeQuantityPercentage
None00.00 %
Empty str00.00 %
String00.00 %
Integer19100.00 %
Float00.00 %
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99R20YME8v8teF3/d3vUxMbeJmSXvzB1gWZZ1MU/cvn27CgoKNGPGDCUlJWnq\n1Kn1Pmfp0qVatmxZjWVjx45VamrqxYxw0dqkv3n+QsuSXKeksHBJ0oG5P5UkFRYW6tChQ4qPj1dg\noPdsOAA11fpzfY5vf679CcczAN7qos9kxsfHS/rmL0ekpaXpkUceUUhISJ3PGTZsmBITE2sOEHyp\nd/y5xoCA6gOypOqZAgPD1LZtrAIDA3Xy5FlVVprzt8uDggIVHn6JUblNzCyZk/u7xxq/zlzH8ax1\n6/Y6fdrlv9kvwK+3dx1MzG1iZsn9uSMimjb6OY0qmQUFBfrss8906623Vi9r3769ysvLdfr0aTVv\n3rzO58fExJx3aTw//5QqKrxvJ6htpsrKKq+c1d1MzG1iZsn/c5v6c32hfCZkP5eJmSUzc5uYWfKu\n3I26XnLo0CE99NBDys3NrV6WnZ2t5s2b11swAQAAYI5Glcz4+Hh17NhRU6ZM0Z49e7R582bNnz9f\nY8aMcdd8AAAA8EGNKplBQUFasWKFLrnkEg0bNkyPPvqoRowYobvv5gblAAAA+K9Gv/GnRYsW571D\nHAAAAPgu7mEBAAAA21EyAQAAYDtKJgAAAGxHyQQAAIDtKJkAAACwHSUTAAAAtqNkAgAAwHaUTAAA\nANiOkgkAAADbUTIBAABgO0omAAAAbEfJBAAAgO0omQAAALAdJRMAAAC2o2QCAADAdpRMAAAA2I6S\nCQAAANtRMgEAAGA7SiYAAABsR8kEAACA7SiZAAAAsB0lEwAAALYLdnoAAABwcVyuUm3atFE7dmxX\nXl6eysvLFBYWpsjIKHXsGK+kpH6Smjo9pq1MzCz5Zm7OZAIA4IN27crR0KEpWrNmlcrKytS2bTvF\nxSWodes2crlcWrPmBd1xR4pycnKcHtU2JmaWfDc3ZzIBAPBBCxbMUWJiP40bN/GC6yxZ8qSmT5+u\nlStXeXAy9zExs+S7uTmTCQCAD9q/f68GDRpc5zqDBg3Wrl27PDSR+5mYWfLd3JRMAAB8ULt27bVh\nw+t1rrN+/Tq1a9fOQxO5n4mZJd/NzeVyAAB8UFpauiZNGq/NmzcpIaGzoqKi1aRJE5WXl6uwsEDZ\n2V/o9OnTevbZZ5we1TYmZpZ8NzclEwAAHxQbe52ysl7Txo3vaOfObO3bt0elpS6FhoYoKipad911\nj269NUmtWsWoqKjE6XFtYWJmyXdzUzIBAPBRYWFhSk5OUXJySq2PBwf736viTMws+WZuSiYAAD4q\nJ2en1q0Tf8zUAAAgAElEQVT70wXvnTh06DD16tXN6TFtZWJmyTdzUzIBAPBB7777tubOzVD//rdr\n+PB7FRHRXCEhISorK9Px44X64ovPNGbMKM2ZM0e9et3s9Li2MDGz5Lu5KZkAAPig559fqYcffuSC\nl09/8pMBSkjopIULF3pV8fg+TMws+W5u77uADwAA6lVcXKy4uIQ61+nQoaPy8/M9NJH7mZhZ8t3c\nlEwAAHxQt27dtXjxAuXmHqv18YKCfC1cOF833XSThydzHxMzS76bm8vlAAD4oMmTpyojY4aGDBmg\nFi2uPOfeiYXKzT2qHj16KSMjw+lRbWNiZsl3c1MyAQDwQeHhl2vevIU6fPiQdu7MVmFhgUpLSxUS\nEqro6Gh17Biv1q3/VxERTb3q3onfh4mZJd/NTckEAMCHtWzZSi1btpIk5eXlKjIySkFBQQ5P5V4m\nZpZ8LzevyQQAwE8MHz5Ux44ddXoMjzIxs+QbuSmZAAD4CcuynB7B40zMLPlGbi6XAwDgw1avfq76\n48rKCr3ySpbCw8MlSfff/4BTY7mViZkl38tNyQQAwIcdPXqk+uOqqirl5+eqpOS0gxO5n4mZJd/L\nTckEAMCHTZkyvfrj99//q37969TqN4f4KxMzS76Xm9dkAgAAwHaUTAAA/MSkSVPUvHmk02N4lImZ\nJd/ITckEAMBP9O7dR19/fVBlZWVe/Vo9O5mYWfKN3LwmEwAAH+dyubRo0Xy99dYbkqTf//5VLV++\nWGVlpVqyZLH88de9iZkl38rNmUwAAHzc008v0f79+7Rq1UsKCQmVJI0c+YCKi4u97u9Z28XEzJJv\n5aZkAgDg4zZvfl/jx6fp6qvbVy+7+ur2Sk+fqi1btjg4mfuYmFnyrdyUTAAAfNyZMyUKDQ07b7ll\nWaqsrHRgIvczMbPkW7kpmQAA+LjevX+kZ59doTNnSiRJAQEBOnLksJ58MlM333yzw9O5h4mZJd/K\nTckEAMDHTZgwWYGBAbr99kSVlp7VyJEj9ItfDNJll4Vr2rRpTo/nFiZmlnwrt/e8BQkAAFyUZs2a\nadas+Tp8+JAOHjygysoKtW7dRldf3U5XXNFURUUlTo9oOxMzS76Vm5IJAIAPO3bsqHbs2K68vDyV\nl5cpLCxMkZFRCg0NdXo0tzExs+R7uSmZAAD4oBMnijVr1uP6+OO/q0WLKxUR0VwhISEqKyvT8eOF\nys/PU+/eP9L8+Znyl1/3JmaWfDe390wCAAAaLDNzls6ePaNXXnlDMTEtzns8N/eYZs+eoWnTpunx\nx+c4MKH9TMws+W5u3vgDAIAP2rZtqyZMmFRr6ZCkFi2u1Pjxafrb3/7m4cncx8TMku/mpmQCAOCD\nIiOjtGfP7jrXycnZqcsvv9xDE7mfiZkl383N5XIAAHzQqFFjlJmZoU8+2abOnbsqKipaTZo0UXl5\nuQoLC/TFF5/r3Xff0hNPPOH0qLYxMbPku7kpmQAA+KCkpNvUsmUrrVv3J61du1qFhYVyuUoVEhKi\nqKhodewYr+XLn1GfPr286rY234eJmSXfzU3JBADAR3XoEKcOHeIu+HhwsP+9Ks7EzJJv5qZkAgDg\no1yuUm3atLHWeyd27BivpKR+kpo6PaatTMws+WZu76u9AACgXrt25Wjo0BStWbNKZWVlatu2neLi\nEtS6dRu5XC6tWfOC7rgjRTk5OU6PahsTM0u+m5szmQAA+KAFC+YoMbGfxo2beMF1lix5UtOnT9fK\nlas8OJn7mJhZ8t3cnMkEAMAH7d+/V4MGDa5znUGDBmvXrl0emsj9TMws+W5uSiYAAD6oXbv22rDh\n9TrXWb9+ndq1a+ehidzPxMyS7+bmcjkAAD4oLS1dkyaN1+bNm5SQ0Pm8eydmZ3+h06dP69lnn3F6\nVNuYmFny3dyUTAAAfFBs7HXKynpNGze+o507s7Vv3x6VlroUGvrNvRPvuuse3Xprklq1ivGqeyd+\nHyZmlnw3NyUTAAAfFRYWpuTkFCUnp9T6uDfeO/H7MjGz5Ju5vW8iAABgC5fLpddee83pMTzKxMyS\nd+amZAIA4KdOnz6t9PR0p8fwKBMzS96Zm5IJAICfioyM9LobdLubiZkl78xNyQQAwAeVl5drxYol\n+vnPf6p+/W7WlCmTdODA/hrrFBYW6vrrr3doQvuZmFny3dyNKpm5ublKTU1V9+7d1adPH82ZM0cu\nl8tdswEAgAtYuXKZtmz5QA8+mKpJk36roqJCjRo1Qlu2fFBjPcuynBnQDUzMLPlu7gaXTMuylJqa\nqrNnz+qll17SwoUL9f7772vRokXunA8AANTi/fc3asqUx3Trrf2VlHSbVqx4QT/72RA99li6Nm3a\nWL1eQECAg1Pay8TMku/mbvAtjPbt26fPPvtMf//73xUVFSVJSk1NVWZmpiZPnuy2AQEAwPlKS0t1\n+eVXVH8eEBCghx4ar8DAQD3xxFQFBQWpc+fODk5oPxMzS76bu8FnMqOjo/X8889XF8xvnT592vah\nAABA3bp2vUHLly9ScXFxjeUPPpiqlJSfa8aMKVq37o8OTeceJmaWfDd3g89khoeHq0+fPtWfV1VV\n6Xe/+5169uzZ4G+Wl5en/Pz8mgMEX6qYmJgGfw1P+e5NTYOCAmv8awoTc5uYWTInt6k/1+fepNmk\n7N/yx8wTJ07Wb3+bpoED+2nhwmXq0eO/v48nTUpXRESEVq9+QZL/5DYxs+S7uQOsi3yVaGZmpl56\n6SW98sorio2NbdBzli5dqmXLltVYNnbsWKWmpl7MCBetTfqb9a5zYO5PPTAJALuY+nNtam4TXWhb\nB5zKkxV2mdTkkvO29d69e/XXv/5Vo0eP9sSItmtIZqnmPu7rmSX/2dYX9Wcl58+frzVr1mjhwoUN\nLpiSNGzYMCUmJtYcIPhSr/o7m9/67kxBQYEKD79EJ0+eVWVllYNTeZaJuU3MLJmT29Sf63OPsSZl\n/5Y/Z7Yu++/VwHO3dXT0/2j06NF+l/u7maWauf01s+Tsto6IaNro5zS6ZM6cOVMvv/yy5s+fr/79\n+zfquTExMeddGs/PP6WKCu/bCWqbqbKyyitndTcTc5uYWfL/3Kb+XF8onwnZz+XvmU3d1ib+bPvC\ntm5UyVy2bJn+8Ic/6KmnntJtt93mrpkAAADg4xpcMvfu3asVK1Zo9OjRuuGGG2q8gSc6OtotwwEA\nAMA3Nbhk/vWvf1VlZaWefvppPf300zUe27Vrl+2DAQAAwHc1uGSOHj3aq96xBAAAAO/lPTdTAgAA\ngN+gZAIAAMB2lEwAAADYjpIJAAAA21EyAQAAYDtKJgAAAGxHyQQAAIDtKJkAAACwHSUTAAAAtqNk\nAgAAwHaUTAAAANiOkgkAAADbUTIBAABgO0omAAAAbEfJBAAAgO0omQAAALAdJRMAAAC2o2QCAADA\ndpRMAAAA2I6SCQAAANtRMgEAAGA7SiYAAABsR8kEAACA7YKdHgDOcrlKtWnTRu3YsV15eXkqLy9T\nWFiYIiOjFB+foCFDfub0iG5xodxRUdHq0eNG9ez5IwUHhzg9pq1M3dYwB/s44F04k2mwXbtyNHRo\nitasWaWysjK1bdtOcXEJat26jVwul1avfl5JSUnavftLp0e1VX25n376ad1xR4r27Nnt9Ki2MXVb\nwxzs44D34UymwRYsmKPExH4aN25irY8HBwdq+fKFysycpZUrV3t4OvepK3dwcKAiIppq2rQZmj9/\ntp55xj9ym7qtYQ72ccD7cCbTYPv379WgQYPrXOfOO+/0qzN6UsNyDxo0WHv3+k9uU7c1zME+Dngf\nSqbB2rVrrw0bXq9znaysLF11VRvPDOQhDcm9fv06tW7dxjMDeYCp2xrmYB8HvA+Xyw2WlpauSZPG\na/PmTUpI6KyoqGg1adJE5eXlKiws0I4d21VSclrz5y9yelRb1ZW7qKhQO3Zs14kTJzVv3kKnR7WN\nqdsa5mAfB7wPJdNgsbHXKSvrNW3c+I527szWvn17VFrqUmhoiKKiojVixD0aNGigyssDVFFR5fS4\ntqkrd0xMjO6//3716NFHoaGXOD2qbUzd1jAH+zjgfSiZhgsLC1NycoqSk1POeyw4OFDNmjVVUVGJ\nA5O514Vyf/vGn6KiEr/7RWTqtoY52McB70LJNFxOzk6tW/enWu8rFxcXr/vuu1ctW7Z1ekzb1ZX7\nhhu6auDAn6t9++ucHtNWpm5rmIN9HPAulEyDvfvu25o7N0P9+9+u4cPvVUREc4WEhKisrEzHjxdq\n+/bPNXz4cD366HTdcsutTo9rm7pyFxcfV05OtsaMGaXf/na6fvzjJKfHtYWp2xrmYB8HvA8l02DP\nP79SDz/8SK2XliRp4MAU9ehxo1auXOZXB+W6cgcHB2rEiDsVG9tBzz673G9KpqnbGuZgHwe8D7cw\nMlhxcbHi4hLqXCchIUEFBQUemsgzGpK7Q4eOKiz0n9ymbmuYg30c8D6UTIN169ZdixcvUG7usVof\nz8/P16xZs9S9e08PT+Ze9eXOzc3VwoXz1a1bDw9P5j6mbmuYg30c8D5cLjfY5MlTlZExQ0OGDFCL\nFleec1+5QuXmHlXv3r01Zco0p0e1VV25jx8v1LFjR9WjRy9Nnuw/uU3d1jAH+zjgfSiZBgsPv1zz\n5i3U4cOHtHNntgoLC1RaWqqQkFBFR0erU6dO6tgx1u9u51NX7iuvbKGbbuquZs2aG5PZn7c1zME+\nDngfSibUsmUrtWzZSpKUl5eryMgoBQUFKTjYv19NUVvu0NAm1ffJ9EembmuYg30c8B781KGG4cOH\n6tixo06P4XEm5jYxM8zCPg44i5KJGizLcnoER5iY28TMMAv7OOAsLpdDq1c/V/1xZWWFXnklS+Hh\n4QoMDFBa2gQHJ3Ov2nJfccXlCgtrorvu+pWDk7mPqdsa5mAfB7wHJRM6evRI9cdVVVXKz89VSclp\nBQYGODiV+9WW+8yZ0woNbeLgVO5l6raGOdjHAe9ByYSmTJle/fH77/9Vv/51qlq2bOX3L5SvLfdV\nV7X26zf+mLqtYQ72ccB78FMHAAAA21EyUcOkSVPUvHmk02N4nIm5TcwMs7CPA86iZKKG3r376Ouv\nD6qsrEwlJaedHsdjvpv79Gkzcpu6rWEO9nHAWbwmE5Ikl8ulRYvm66233pAk/f73r2rFisWqrCzX\nY49l6NJLmzk8oXucm/uPf/yzZs5cqlOnTuuxx2YpPDzc4QntZ+q2hjnYxwHvwJlMSJKefnqJ9u/f\np1WrXlJISKgk6f77x6ioqEhPPTXP4encp7bcv/nNb1RcXKzFi+c7PJ17mLqtYQ72ccA7UDIhSdq8\n+X2NH5+mq69uX72sfftrNHPmTG3d+pGDk7lXbbmvvfZapadP1ccf+2duU7c1zME+DngHSiYkSWfO\nlCg0NOy85VVVVaqsrHBgIs+4UG7LslRZWenARO5n6raGOdjHAe9AyYQkqXfvH+nZZ1fozJlv7g8Z\nEBCgI0cOKyMjQzfd1Mfh6dynttxff/21nnwyU7169XZ4OvcwdVvDHOzjgHegZEKSNGHCZAUGBuj2\n2xNVWnpWI0eO0JAhKQoPD9fEiY84PZ7bnJv73nvvUr9+/XTZZeGaMGGS0+O5hanbGuZgHwe8A+8u\nhySpWbNmmjVrvg4fPqSDBw+osrJCbdu2VZcucSoqKlFFRZXTI7rFubmlKsXFXafmza80JrMp2xrm\nYB8HvAMlEzp27Kh27NiuvLw8lZeXKSwsTJGRUQoNDXV6NLeqLXd0dLTCws5/LZe/MHVbwxzs44D3\noGQa7MSJYs2a9bg+/vjvatHiSkVENFdISIjKysp0/Hih8vPz1LdvXz3yyFS/uq9cfblnzMhT794/\n0uTJ0/zmPpmmbmuYg30c8D6UTINlZs7S2bNn9MorbygmpsV5jxcW5mn27Mc1d26GnnhirgMTukdd\nuYODA1VaelITJ6Zp3rxZysjIdGhKe5m6rWEO9nHA+/DGH4Nt27ZVEyZMqvWALEktWlypKVOm6OOP\nt3p4MveqL/cPfvADjR+fpm3bPvbwZO5j6raGOdjHAe9DyTRYZGSU9uzZXec62dnZCg+/zEMTeUZD\ncufk7NRll/lPblO3NczBPg54Hy6XG2zUqDHKzMzQJ59sU+fOXRUVFa0mTZqovLxchYUFys7+XH/5\ny1uaPPlRp0e1VV25i4uPKycnW+vXr1da2m+dHtU2pm5rmIN9HPA+lEyDJSXdppYtW2nduj9p7drV\nKiwslMtVqpCQEEVFRSs+PkEvvviirrrqGr+65UdduaOjY9S1axctX/6MrrsuzulRbWPqtoY52McB\n70PJNFyHDnHq0KH2MhUcHKiIiKYqKirx8FTud6Hc383sb7+ITN3WMAf7OOBdKJmGc7lKtWnTxlrv\nK5eQ0EmDB6c4PaJbXCh3VFS0evbsph49+ig4OMTpMW1l6raGOdjHAe/CG38MtmtXjoYOTdGaNatU\nVlamtm3bKS4uQa1bt5HL5dKqVc8pKSlJu3d/6fSotqov94oVK3THHSn1vonAl5i6rWEO9nHA+3Am\n02ALFsxRYmI/jRs3sdbHg4MDtXz5QmVmztLKlas9PJ371JX720tq06bN0Pz5s/XMM/6R29RtDXOw\njwPehzOZBtu/f68GDRpc5zp33nmnX53RkxqWe9Cgwdq7139ym7qtYQ72ccD7UDIN1q5de23Y8Hqd\n62RlZemqq9p4ZiAPaUju9evXqXXrNp4ZyANM3dYwB/s44H24XG6wtLR0TZo0Xps3b1JCQufz7iu3\nY8d2lZSc1vz5i5we1VZ15S4qKtSOHdt14sRJzZu30OlRbWPqtoY52McB70PJNFhs7HXKynpNGze+\no507s7Vv3x6VlroUGvrNfeVGjLhHgwYNVHl5gF/dzqeu3DExMbr//vvVo0cfhYZe4vSotjF1W8Mc\n7OOA96FkGi4sLEzJySlKTj7/1h7BwYFq1sw/7yt3odz+fJ9MU7c1zME+DngXXpOJOrlcLr311gan\nx/A4l8ult982K7ep2xrmYB8HPIuSiTqdOnVKM2dOd3oMjzt9+rRmz37c6TE8ytRtDXOwjwOeRclE\nnaKiorR166dOj+FxkZGR+vDDfzo9hkeZuq1hDvZxwLMuumSWlZUpOTlZ//jHP+ycBwAAAH7got74\n43K5NHHiRO3ezU1tfdlnn/2/Oh8PCgrQZZddolOnzio+vouHpnK/unJ/N3NlpaXOnbt6cDL3MXVb\nwxzs44D3aXTJ3LNnjyZOnCjLstwxDzzoqacydeDAfkmqc3sGBARoy5ZtnhrL7UzMbWJmmIV9HPA+\njS6Z27ZtU48ePTRhwgR17tzZHTPBQ55/fq1mzHhUR48e1sqVqxUaGlrjcX+9nU9duU3MLPlvbpiD\nfRzwPo0umb/85S8v+pvl5eUpPz+/5gDBlyomJuaiv6a7BAf/9+WqQUGBNf71F8HBYcrImKNRo+7R\nCy+sVGrqhBqPm5jbxMyS/+Y+lwk/17X5bm7JP7Ozj3/DhG1dGxN/tn1hW3v0ZuxZWVlatmxZjWVj\nx45VamqqJ8dokIiIpuctCw/33b8A0yb9zQs+FvC/P9OOnXs1vZbMkpm5/T3z6swPdGDuT8973Jdz\nN4S//Vw3VG25Jd/NburxrCH8bVs3lIk/276wrT1aMocNG6bExMSaAwRf6pV/geG7MwUFBSo8/BKd\nPHlWlZX+d5nFCm8hK7zFedvBxNymZJbM2se/ZWJmSUb9bJt6PPsWuc3MLLk/94VKbV08WjJjYmLO\nuzSen3/KK18fU9tMlZVVXjmrXS6UzcTc/p5ZMjO3iZklM3+2Tcwskfu7TMwseVdu77lwDwAAAL9B\nyQQAAIDtKJkAAACw3fd6TeauXbvsmgMAAAB+hDOZAAAAsB0lEwAAALajZAIAAMB2lEwAAADYjpIJ\nAAAA21EyAQAAYDtKJgAAAGxHyQQAAIDtKJkAAACwHSUTAAAAtqNkAgAAwHaUTAAAANiOkgkAAADb\nUTIBAABgO0omAAAAbEfJBAAAgO0omQAAALAdJRMAAAC2o2QCAADAdpRMAAAA2I6SCQAAANtRMgEA\nAGA7SiYAAABsR8kEAACA7SiZAAAAsB0lEwAAALajZAIAAMB2lEwAAADYjpIJAAAA21EyAQAAYDtK\nJgAAAGxHyQQAAIDtKJkAAACwHSUTAAAAtqNkAgAAwHaUTAAAANiOkgkAAADbUTIBAABgO0omAAAA\nbEfJBAAAgO0omQAAALAdJRMAAAC2o2QCAADAdpRMAAAA2I6SCQAAANtRMgEAAGA7SiYAAABsR8kE\nAACA7SiZAAAAsB0lEwAAALajZAIAAMB2lEwAAADYjpIJAAAA21EyAQAAYDtKJgAAAGxHyQQAAIDt\nKJkAAACwHSUTAAAAtqNkAgAAwHaUTAAAANiOkgkAAADbUTIBAABgO0omAAAAbEfJBAAAgO0omQAA\nALAdJRMAAAC2o2QCAADAdpRMAAAA2I6SCQAAANtRMgEAAGA7SiYAAABsR8kEAACA7RpdMl0ul6ZM\nmaIbb7xRvXv31qpVq9wxFwAAAHxYcGOfMG/ePGVnZ2vNmjU6cuSIJk+erP/5n//Rbbfd5o75AAAA\n4IMaVTLPnDmjP/3pT3ruuefUsWNHdezYUbt379ZLL71EyQQAAEC1Rl0uz8nJUUVFhbp06VK97IYb\nbtDnn3+uqqoq24cDAACAb2rUmcz8/HxFREQoJCSkellUVJRcLpeKi4vVvHnzOp+fl5en/Pz8mgME\nX6qYmJjGjOERwcH/7d9BQYE1/vVX380smZnblMySmblNzCyZ+bNtYmaJ3JKZmSUvzW01wp///Gfr\nlltuqbHsq6++smJjY62jR4/W+/wlS5ZYsbGxNf5bsmRJY0ZwRG5urrVkyRIrNzfX6VE8ysTcJma2\nLDNzm5j5WyZmNzGzZZmZ28TMluWduRtVd0NDQ1VWVlZj2befh4WF1fv8YcOGad26dTX+GzZsWGNG\ncER+fr6WLVt23llYf2dibhMzS2bmNjHzt0zMbmJmyczcJmaWvDN3oy6Xt2jRQkVFRaqoqFBw8DdP\nzc/PV1hYmMLDw+t9fkxMjFdeGgcAAIC9GnUm8/rrr1dwcLA+++yz6mWffvqp4uPjFRjoRa8BAAAA\ngKMa1QwvueQS/exnP9OMGTP0xRdfaOPGjVq1apXuvvtud80HAAAAHxQ0Y8aMGY15Qs+ePbVz5049\n+eST2rp1q8aMGaPBgwe7aTzv0bRpU3Xv3l1NmzZ1ehSPMjG3iZklM3ObmPlbJmY3MbNkZm4TM0ve\nlzvAsizL6SEAAADgX3ghJQAAAGxHyQQAAIDtKJkAAACwHSUTAAAAtqNkAgAAwHaUTAAAANiOkgkA\nAADbUTIBAABgO0rmf5SVlSk5OVn/+Mc/qpdlZGTo2muvrfHf7373u+rHBw4ceN7jX375pRPjN1pu\nbq5SU1PVvXt39enTR3PmzJHL5aqxzqlTp9SnTx+tW7euxvLVq1frlltuUadOnTRy5EgdOHDAg5Nf\nvIMHD2rkyJHq0qWLbrnlFj3//PPVj3344YcaOHCgEhISNHDgQG3evLnWr/H6669rxIgRnhrZVqNH\nj1Z6evp5yw8ePKiEhITzlr/22mvq37+/unbtqrFjxyo/P98TY9rivffeO+9nMzU1VZL09ddf6957\n71Xnzp31k5/8RH/729+qn5eYmHje86699lotW7bMqSiNVlf27OxsDRs2TF26dNHQoUP12Wef1Xju\ntm3blJKSok6dOmno0KHKyclxIkKjlZWV6fHHH1e3bt1000036amnntK3f2fkk08+0c9//nN17txZ\nKSkp+uijj2r9Gp9//rmuv/56HTp0yJOjX7R169bVuq9ed911Sk9Pr/Wx7/4J6FdffVW33XabunTp\nojvuuEOffvqpg2ka7ujRo3rggQfUtWtXJSYm6v/+7/+qHzty5Ijuv/9+derUSUlJSXrrrbdqPPej\njz5ScnKyOnXqpLvvvltff/21h6e/eIWFhUpNTdWNN96opKSk834vSxf+nf3yyy/rxz/+sbp27aqR\nI0d6NrcFq7S01Bo7dqwVGxtrffzxx9XL7733XuuZZ56x8vLyqv87c+aMZVmWVVFRYcXHx1vbtm2r\n8Xh5eblTMRqsqqrKGjp0qDVq1Cjryy+/tP75z39aSUlJ1ty5c2usN23aNCs2NtZ69dVXq5etX7/e\nuuGGG6wPPvjA2r9/v/Xwww9b/fv3t6qqqjwdo1EqKyutfv36WRMnTrT2799vffDBB1bXrl2t119/\n3Tpw4ICVkJBgrV692vrqq6+sVatWWR07drS+/vrrGl9j69atVqdOnazhw4c7lOLibdiwwYqNjbUm\nT55cY/mRI0es/v37W7GxsTWWb9myxbr++uuttWvXWnv27LHS0tKslJQUq7Ky0pNjX7QVK1ZYDzzw\nQI2fzRMnTlhVVVXWgAEDrIkTJ1p79uyxVq5caXXq1Mk6fPiwZVmWVVhYWOM5a9eutW644Qbr0KFD\nDidquAtlLygosG644QZr6tSp1p49e6zVq1dbnTt3rs7+1VdfWQkJCdbSpUut/fv3W1OnTrX69u1r\nuVwuhxPVb9q0aVa/fv2szz//3Proo4+sHj16WC+//HJ15ueee8766quvrKefftrq1KmTdfTo0RrP\nLysrs5KTk63Y2Njzfu691dmzZ2ts4yNHjlhJSUnWrFmzrJMnT9Z47F//+pcVFxdnvffee5ZlWdbm\nzZuthIQEa/369daBAweshQsXWl27drWOHTvmcKr6DR061Bo/fry1f/9+67333rM6depkvfvuu1Z5\nebmVnJxsjRkzxtq7d6/18ssvWx07drR27dplWZZlHT582OrcubP1wgsvWF9++aU1btw4Kzk52et/\nd1nWN7+zhw0bZt1xxx3Wjh07rE2bNlndunWz3nnnnRrr1fY7e8uWLVaXLl2sTZs2Wfv27bMeeugh\na/rQMk4AABHJSURBVMCAAR6b3fiSuXv3bmvgwIHWgAEDziuZffr0sT788MNan3fgwAHruuuus0pL\nSz01qm327NljxcbGWvn5+dXL3njjDat3797Vn39bPH/4wx/W2GF/97vfWX/4wx+qP//3v/9txcbG\nWgUFBZ4Z/iLl5uZa48aNs06dOlW9bOzYsdb06dOtjz/+2MrIyKixfrdu3aw333yz+vOlS5dacXFx\nVnJyss+VzKKiIutHP/qRNXjw4Bol87333rN69uxZve9/1+jRo61HHnmk+vOzZ89a3bt3t7Zs2eKx\nub+PiRMnWk8++eR5yz/66COrc+fOVklJSfWye+65x1qyZMl56548edLq2bOn9cc//tGts9rtQtmf\nf/5568c//rFVUVFRvWzkyJHWggULLMuyrNmzZ9fYt8+cOWP9+Mc/tv7973+7f+jvoaioyOrQoYP1\nj3/8o3rZM888Y6Wnp1vvvvuu1b179xrrd+/e3Xr77bdrLFuxYoX1i1/8wqdK5rlWrlxp3XrrrbX+\nT8F9991npaWlVX8+fvx467HHHquxTr9+/aysrCy3z/l9FBcXW7GxsdXF0bIs66GHHrIef/xxa+PG\njdYNN9xQ4xj/61//uvr31aJFi87bv7t06VLjd763+uKLL6zY2Fjrq6++ql72zDPPWEOHDq3+/EK/\ns2fOnGn95je/qf48JyfHio2NtQoLCz0y+/9v596jYk7/OIC/k+1iLbOlscTPJbdmmiaVUDhqqURW\nnFw2VqxjsWldNraDcrb2dNw2i7OmaAu1RZZodw8abYrNplREVNPF2FTsQUVqpj6/Pzq+x9ektSs1\nc/Z5ndMf83wvno/vPN/v832eZz7/+enyrKwsjB07FkePHuWV19fXo7q6GoMHD27zuJKSEvTr1w+G\nhoadUMuOZWZmhoMHD6JPnz688vr6egCtU09btmxBUFAQDAwMePv4+Phg3rx5AFqH5n/88UcMHz4c\nJiYmnVP5f0koFGL37t3o2bMniAg5OTm4cuUKHBwcMHbsWGzatAkAoFKpkJiYiKamJt4U8qVLlxAV\nFQVXV9euCuFf27ZtGz766CMMGzaMV56WloYvvviCi/1FSqWSF7+RkRH+97//aUyvaiuFQtFm283P\nz4dIJEKPHj24Mjs7uzbjioqKgpmZGebMmfM2q9rhXhW7UqmEWCyGvr4+VzZy5Egu9qysLN7329jY\nGHK5HKNGjXrrdX4TOTk56NmzJxwcHLiy5cuXIywsDAKBAI8ePcK5c+dARJDL5Xjy5AlGjBjB7VtW\nVoa4uLg2l5LoikePHuHAgQNYv369xj07MzMTV65cwbp167iyZcuWYcmSJRrnqaure+t1fRNGRkYw\nNjbGiRMnoFKpUFpaiqtXr8LS0hJZWVkYP348evbsye3//fffc8+r/Px82Nvbc9uMjY0hFot14p6m\nVCphYmKCgQMHcmUjR45EQUEBVCpVu89sgUCAK1euQKFQQK1WIykpCebm5ujdu3en1L17p/wrWuzj\njz9us1yhUEBPTw8ymQzp6ekQCARYsmQJvLy8uO3vvPMOPvvsMxQUFGDIkCHYsGFDm2vbtE2vXr0w\nceJE7nNLSwtiY2Mxbtw4AIBMJoNIJMKECRNeeY7jx49j06ZNMDAwQFRUFPT09N56vTuKi4sLKisr\n4ezsDDc3N668oqIC06ZNQ3NzM9avX48BAwZw2+Lj4wGAt2ZXF2RmZiI7OxvJycnYunUrb1toaCiA\ntmMyNTVFTU0N97mlpQXV1dV4+PDhW61vRyAilJWV4eLFi4iIiEBzczPc3d3h7++P+/fvQygU8vY3\nNTVFVVUVr6yhoQGxsbH4+uuv0a2b7ryLtxd7nz59NNZYVlVVcddUqVTCyMgI/v7+yM7OxrBhwxAU\nFKTxcqJtlEolzM3NkZSUBJlMBpVKhdmzZ2PlypWwt7eHj48P/P390a1bNzQ3NyMsLAxDhw4F0Pr/\nFRQUhNWrV8PU1LSLI/n34uPjIRQK4e7urrEtMjISXl5e6NevH1cmFot5+6Snp6O8vJx7BmgrQ0ND\nBAUFISQkBIcPH0ZzczNmz54Nb29vrFq1Cubm5ti5cydOnTqF999/H/7+/pgyZQoAvHbb10Z9+vRB\nXV0dGhoaYGxsDKC17arVatTV1SE2NvaVz+xFixYhMzMTHh4e0NfXh7GxMeLi4ngvm2+T7tw9O1lp\naSn09PQwdOhQREZGwtvbG1u2bEFKSgqA1rffx48fw9vbG5GRkbCwsMDixYtx7969Lq75P7djxw7c\nvHkTa9euRUlJCRISEhAYGNjuMY6Ojjh58iTmzp2LVatW6dQC6j179kAmk6GwsBBhYWFcuYmJCY4f\nP46goCDs3bsXZ8+e7cJavrnGxkYEBwcjKCgIRkZG/+hYDw8PxMfHIzc3FyqVCjKZDH/99RdUKtVb\nqm3HqaysRENDAwwMDLB7925s3LgRycnJ2L59O1f+IgMDAzQ1NfHKfv31V/To0UPnRq7bi93V1RXX\nrl3DsWPHoFarkZGRgfPnz3PX9OnTp9i5cyfGjBmDAwcOoF+/fvD19cWTJ0+6OKr2PX36FBUVFUhI\nSEBYWBg2btyII0eOICYmBk+ePIFSqYSfnx8SExOxYsUKhIaGQqFQAGh9WVapVJg7d24XR/HvERES\nExOxcOFCjW1KpRKXL19u98eKd+7cQWBgIDw9PTU6n9pIoVDA2dkZR48eRVhYGM6cOYPTp0/j6dOn\nOHnyJGprayGTyTBr1iz4+/vj+vXrAPDabV8bSaVSCIVChISEcN/36OhoAEB5eXm7z+yamho0NjZi\n586dSEhIwJgxYxAQEKDxQ9+35T8/kvkqs2bNgrOzMwQCAQBg1KhRKC8vR3x8PKZOnYqQkBA8e/aM\nG5rfunUrrl69ilOnTmHFihVdWfV/ZMeOHTh06BDCw8MxfPhwLFiwgBv1aE///v3Rv39/bpoiKSkJ\nq1ev7qRavxmJRAKgtRP25ZdfYsOGDTAwMMB7770HkUgEkUgEhUKB2NhY3kinrtm3bx+srKx4o9av\na+7cuSgqKoKPjw8AwM3NDZMmTeJNRWkrc3Nz/PHHH+jduzf09PRgaWmJlpYWBAQEwMvLCw0NDbz9\nm5qaNDrhZ8+ehYeHB7p3161bZHuxBwYGIiQkBKGhoQgODoalpSUWLFjAjWTr6+vDxcWF65CEhIRg\n8uTJSE1NhaenZ1eG1a7u3bujvr4eu3btgrm5OYDWznZ8fDxqa2tBRPDz8wPQOoJ37do1HD58GH5+\nfggPD0dMTIxOzcS87Pr166iursb06dM1tp09exaWlpavHI0uKyvDkiVLMHDgQG5mQ5tlZmbi+PHj\nuHDhAoyMjCCRSFBdXY39+/ejf//+EAgE2Lp1K7p16waxWIzs7GwcO3YMEokEhoaGGh3KpqYm9OrV\nq4uieX2GhobYvXs31qxZAzs7O5iammLZsmUICwtDaGhou8/s4OBguLq6cm14165dmDx5Ms6fPw8P\nD4+3Xnc2kvkKenp6XAfzuaFDh6K6uhpA643txQfu81HP59t1QUhICKKjo7Fjxw64ubmhsrISubm5\n2LZtG0aPHo3Ro0ejsrISwcHBWLZsGQDg8uXLKC0t5c7xPG5tn0Z98OAB5HI5r2zYsGFQqVTIy8tD\ndnY2b5uFhYXWx/R3fvnlF8jlcu5aJicnIzk5GaNHj/7bY/X19REcHIycnBz8/vvvCA8Px/3797mH\nuLYTCAS8joOFhQUaGxthZmaGBw8e8PZ98OABbxqtqakJWVlZ3DSbrnlV7I8fP8acOXOQnZ2NCxcu\n4MSJE9DT0+OWhZiZmWHIkCHccQYGBjA3N9f62RkzMzMYGhryvptDhgzBvXv3cOPGDY01pZaWlqis\nrMTFixfx8OFDLqXTjBkzAAAzZsyATCbr1BjeREZGBuzt7dtcY5eRkYEPP/ywzeOKi4uxcOFCfPDB\nBzh48OA/nu3oCgUFBRg0aBCvriKRCJWVlRAKhRg8eDBvecvz7wEA9O3bt822b2Zm1jmVf0PW1tZI\nTU1Feno60tLSuLZ648aNdp/ZL7eBd999F4MGDcKff/7ZKfVmncxX+O677+Dr68sru3XrFreWZ9Gi\nRbzceS0tLbh9+za3Xdvt27cPCQkJ+Pbbb7k34L59++LcuXNISkri/oRCIfz9/fHNN98AAA4cOMDL\nS9bc3Ixbt27BwsKiK8J4bXfv3oWfnx/vJaCgoAAmJibIy8vD5s2bubx6QGvD1JVr+SpHjhxBcnIy\ndy1dXFzg4uKCpKSkvz02JiYGkZGRMDY2hkAgQE1NDQoLC3k/rtBWGRkZGDt2LG/EsrCwEAKBAHZ2\ndrhx4waePXvGbcvJyYFUKuU+3759G2q1WifWV7+svdiLioqwdu1a6OvrQygUgoi4/QHAxsYGt2/f\n5o5ramqCUqnkrU3WRlKpFI2NjSgrK+PKSktLYW5uDqFQiJKSEt7+paWlGDBgAKZOnYozZ85w7SMy\nMhJA6xrG+fPnd2oMb+LatWuwtbXVKCciXL9+vc1tNTU1WLp0KQYNGoSoqCidmKEAWn/AWVFRwRuR\nfH49pVIpiouL0dzczG1TKBTcy4dUKuXlAm1oaMDNmzd5bV9bPXr0CAsWLMDDhw9hZmaG7t27Iy0t\nDVOmTPnbZ7ZQKOSWhwCt7fru3bud16475TfsOuLFFEb5+fkkEono4MGDVFFRQXFxcWRlZUVXr14l\nIqIffviB7OzsSC6Xk0KhoODgYHJ0dOSlT9BWJSUlZGlpSeHh4bxcajU1NRr7Ojs789IhyOVyEovF\ndPr0aVIoFLR582aaNGkS1dfXd2YI/5harabZs2fT0qVLqbi4mNLS0sjR0ZFiYmLo3r17ZGtrS9u3\nb6eysjKKjY0lsVhMBQUFGufZs2ePzqUwem7jxo0aeTKJiC5fvqyRwiglJYXs7e0pMzOTioqKaN68\nebRy5crOquobqauro4kTJ9K6detIoVBQWloaTZgwgSIjI0mtVpOHhwetWbOGioqKKCIigpcrkojo\np59+Ijc3ty6M4N9rL/aqqiqSSqUUFxdHd+7coeDgYJo4cSLXdvPy8kgsFlNcXByVlZXRpk2baNKk\nSbx0T9pq+fLlNG/ePCosLKT09HQaN24cHTp0iHJzc8nS0pLLgRsdHU1isZiKioo0zqFUKnUyhZGz\nszP9/PPPGuXP42nrvr5u3TpydHSk0tJS3v1f2+/jtbW15OTkRAEBAVRaWkrnz58nBwcHio+Pp7q6\nOpowYQJt2bKFysvLKTY2lkQiEXcfVyqVJJFIKCIigsuT6enpqRN5MomIZs6cSYGBgXTnzh06duwY\nSSQSys/P19jv5Wd2REQEOTg4UGpqKikUCtqwYQO5uLh0WvpF1sl8wct5MlNSUsjT05MkEgm5u7vz\nEp+2tLTQ/v37afLkyWRlZUU+Pj683F3aLCIigkaMGNHm38te/sISESUmJpKrqytJJBJatGgRlZSU\ndFbV30hVVRV9/vnnZGtrS05OTrR//37uBpObm0ve3t5kbW1N06ZNI7lc3uY5/iudTKLWvHtOTk5k\nb29PX331lU68QD1XVFREvr6+ZGNjQ05OTrR3717uWpeXl5OPjw9ZWVnR9OnT6dKlS7xjX84/p2va\ni/23334jd3d3kkql9Mknn2i03ZSUFHJzcyMrKyuaP39+m50xbVRbW0sBAQFkY2ND48eP58Usl8tp\n5syZZGNjQ15eXhrX+zld7WRKJJI289fm5eXRiBEjNPJmtrS0kLW1dZv3/7byxWqb4uJi8vX1JVtb\nW5oyZQpFR0dz17q4uJhr266urhrJytPS0sjV1ZWsra1p8eLFvLyT2k6hUNDChQtJKpXS9OnTKTU1\ntc39Xn5mq9VqioiIIBcXF7K1taVPP/20U+PWI3phjpBhGIZhGIZhOgBbk8kwDMMwDMN0ONbJZBiG\nYRiGYToc62QyDMMwDMMwHY51MhmGYRiGYZgOxzqZDMMwDMMwTIdjnUyGYRiGYRimw7FOJsMwDMMw\nDNPhWCeTYRiGYRiG6XCsk8kwDMMwDMN0ONbJZBiGYRiGYToc62QyDMMwDMMwHY51MhmGYRiGYZgO\n93/ad+3RW+1nsAAAAABJRU5ErkJggg==\n", 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AAIBXIYRWo3nzllq27B2tWbNaO3du1759e+R0OhUaGqK6dSM1YMDv1K1bd4WE1LG7VAAA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\nnI63wciRo5WWNkzr1q1VUlJ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06Ej95z//NjhZ+bE9I/ncz/aM5HM/2zPans/fKKUoF6dOnVRoaFix5Y5ToPz8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\n7zEk4r+frYmv7xkwmUz49NNPsXXrVrS0tMDBwcHye7YrV66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AAKajZFZT7959FR19mVavflOvvLJChYWFcjpL5HA4FB4eobi4BM2f/7zi4xPMHhUAAMB0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\nCAkJgZOTE6Kjo1FRUYGtW7dKHa/dIiMjkZ2djT/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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3153,25 +2917,33 @@ "name": "stdout", "output_type": "stream", "text": [ - "end of __analyze 1.987433910369873\n" + "end of __analyze 2.0616061687469482\n" ] }, { "data": { "text/html": [ - "
Column name: age
Column datatype: string
DatatypeQuantityPercentage
None00.00 %
Empty str00.00 %
String00.00 %
Integer00.00 %
Float20100.00 %
" + "
Column name: price
Column datatype: int
DatatypeQuantityPercentage
None00.00 %
Empty str00.00 %
String00.00 %
Integer19100.00 %
Float00.00 %
" ], "text/plain": [ - "" + "" ] }, "metadata": {}, "output_type": "display_data" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Min value: 20\n", + "Max value: 200\n" + ] + }, { "data": { "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3179,9 +2951,9 @@ }, { "data": { - "image/png": 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reoc1WHM9N9aYMfebHQI+yhf2+SDn5S9cqkNBQYGuuuqq/3sqStqyZYumTJmi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\n4eHhGjx4iAYPrv6SPVa9TmZt47bZgtW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33nizJOn6629QmzZtNHXqi5RMmKLKp8vz8vL0wQcfaPLkyYqPj1eXLl302GOP6YcffqjN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\nTLVL5uTJk/Xuu+8qNTVVvXr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Column name: product
Column datatype: string
DatatypeQuantityPercentage
None00.00 %
Empty str00.00 %
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AGI6RCQAAAMMxMgEAAGA4RiYAAAAMx8gEAACA4RiZAAAAMBwjEwAAAIZjZAIA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\nRU5ErkJggg==\n", 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hpkeocLZnJJ/72Z7R9nyS/RnJ536BkNEffpXS0NDQEuXz7O2wsDCf5dWrV1fz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\n/e9Ox44dnZkzZzrLly930tPTnYSEBFe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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3229,17 +3001,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "end of __analyze 1.5678620338439941\n", - "Total execution time: 16.03752112388611\n" + "end of __analyze 2.054389715194702\n", + "Total execution time: 26.242274045944214\n" ] }, { "data": { "text/html": [ - "" + "
General description
FeaturesName or Quantity
File Namefoo.csv
Columns8
Rows20
" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3249,8 +3021,7 @@ "source": [ "# Setting the new dataFrame transformed into the analyzer class\n", "analyzer.set_data_frame(transformer.get_data_frame)\n", - "analyzer_table = analyzer.column_analyze(['id','firstName','lastName','billingId','birth','age','product'], \n", - " print_type=True, plots=True)" + "analyzer_table = analyzer.column_analyze(\"*\", print_type=True, plots=True)" ] }, { @@ -3291,20 +3062,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "+----+---------+--------+-------+---------+-----+----------+--------+\n", - "| id|firstName|lastName| age|billingId|price| birth| product|\n", - "+----+---------+--------+-------+---------+-----+----------+--------+\n", - "| 1| luis| alvarez|37.7177| 123| 200|07-07-1980| cake|\n", - "| 2| andre| ampere|67.7124| 423| 160|08-07-1950| piza|\n", - "| 3| niels| bohr|27.7151| 551| 160|09-07-1990| pizza|\n", - "| 4| paul| dirac|63.7258| 521| 160|10-07-1954| pizza|\n", - "|null| albert|einstein|27.7151| 634| 160|11-07-1990| pizza|\n", - "| 6| galileo| galilei|87.7204| 672| 100|12-08-1930| arepa|\n", - "| 7| carl| gauss|47.7231| 323| 60|13-07-1970| taco|\n", - "|null| david| hilbert|67.7124| 624| 60|14-07-1950|taaaccoo|\n", - "| 9| johannes| kepler|97.7231| 735| 60|22-04-1920| taco|\n", - "| 10| james| maxwell|94.7151| 875| 60|12-03-1923| taco|\n", - "+----+---------+--------+-------+---------+-----+----------+--------+\n", + "+---+---------+--------+-------+---------+-----+----------+--------+\n", + "| id|firstName|lastName| age|billingId|price| birth| product|\n", + "+---+---------+--------+-------+---------+-----+----------+--------+\n", + "| 1| luis| alvarez|37.7581| 123| 200|07-07-1980| cake|\n", + "| 2| andre| ampere|67.7527| 423| 160|08-07-1950| piza|\n", + "| 3| niels| bohr|27.7554| 551| 160|09-07-1990| pizza|\n", + "| 4| paul| dirac|63.7661| 521| 160|10-07-1954| pizza|\n", + "| 5| albert|einstein|27.7554| 634| 160|11-07-1990| pizza|\n", + "| 6| galileo| galilei|87.7608| 672| 100|12-08-1930| arepa|\n", + "| 7| carl| gauss|47.7634| 323| 60|13-07-1970| taco|\n", + "| 8| david| hilbert|67.7527| 624| 60|14-07-1950|taaaccoo|\n", + "| 9| johannes| kepler|97.7634| 735| 60|22-04-1920| taco|\n", + "| 10| james| maxwell|94.7554| 875| 60|12-03-1923| taco|\n", + "+---+---------+--------+-------+---------+-----+----------+--------+\n", "only showing top 10 rows\n", "\n" ] @@ -3342,30 +3113,29 @@ "name": "stdout", "output_type": "stream", "text": [ - "+----+------------+--------+--------+---------+-----+----------+----------+\n", - "| id| firstName|lastName| age|billingId|price| birth| product|\n", - "+----+------------+--------+--------+---------+-----+----------+----------+\n", - "| 1| luis| alvarez| 37.7177| 123| 200|07-07-1980| cake|\n", - "| 2| andre| ampere| 67.7124| 423| 160|08-07-1950| pizza|\n", - "| 3| niels| bohr| 27.7151| 551| 160|09-07-1990| pizza|\n", - "| 4| paul| dirac| 63.7258| 521| 160|10-07-1954| pizza|\n", - "|null| albert|einstein| 27.7151| 634| 160|11-07-1990| pizza|\n", - "| 6| galileo| galilei| 87.7204| 672| 100|12-08-1930| arepa|\n", - "| 7| carl| gauss| 47.7231| 323| 60|13-07-1970| taco|\n", - "|null| david| hilbert| 67.7124| 624| 60|14-07-1950| taco|\n", - "| 9| johannes| kepler| 97.7231| 735| 60|22-04-1920| taco|\n", - "| 10| james| maxwell| 94.7151| 875| 60|12-03-1923| taco|\n", - "|null| isaac| newton| 18.7258| 992| 180|15-02-1999| pasta|\n", - "| 12| emmy| noether| 24.7258| 234| 180|08-12-1993| pasta|\n", - "| 13| max| planck| 23.7285| 111| 80|04-01-1994|hamburguer|\n", - "| 14| fred| hoyle| 20.7204| 553| 160|27-06-1997| pizza|\n", - "| 15| heinrich | hertz| 61.7124| 116| 160|30-11-1956| pizza|\n", - "| 16| william| gilbert| 59.7204| 886| 40|26-03-1958| beer|\n", - "| 17| marie| curie| 17.7285| 912| 20|22-03-2000| rice|\n", - "| 18| arthur| compton|118.7124| 812| 100|01-01-1899| null|\n", - "| 19| james|chadwick| 96.7285| 467| 200|03-05-1921| null|\n", - "| 19| james|chadwick| 96.7285| 467| 200|03-05-1921| null|\n", - "+----+------------+--------+--------+---------+-----+----------+----------+\n", + "+---+------------+--------+--------+---------+-----+----------+----------+\n", + "| id| firstName|lastName| age|billingId|price| birth| product|\n", + "+---+------------+--------+--------+---------+-----+----------+----------+\n", + "| 1| luis| alvarez| 37.7581| 123| 200|07-07-1980| cake|\n", + "| 2| andre| ampere| 67.7527| 423| 160|08-07-1950| pizza|\n", + "| 3| niels| bohr| 27.7554| 551| 160|09-07-1990| pizza|\n", + "| 4| paul| dirac| 63.7661| 521| 160|10-07-1954| pizza|\n", + "| 5| albert|einstein| 27.7554| 634| 160|11-07-1990| pizza|\n", + "| 6| galileo| galilei| 87.7608| 672| 100|12-08-1930| arepa|\n", + "| 7| carl| gauss| 47.7634| 323| 60|13-07-1970| taco|\n", + "| 8| david| hilbert| 67.7527| 624| 60|14-07-1950| taco|\n", + "| 9| johannes| kepler| 97.7634| 735| 60|22-04-1920| taco|\n", + "| 10| james| maxwell| 94.7554| 875| 60|12-03-1923| taco|\n", + "| 11| isaac| newton| 18.7661| 992| 180|15-02-1999| pasta|\n", + "| 12| emmy| noether| 24.7661| 234| 180|08-12-1993| pasta|\n", + "| 13| max| planck| 23.7688| 111| 80|04-01-1994|hamburguer|\n", + "| 14| fred| hoyle| 20.7608| 553| 160|27-06-1997| pizza|\n", + "| 15| heinrich | hertz| 61.7527| 116| 160|30-11-1956| pizza|\n", + "| 16| william| gilbert| 59.7608| 886| 40|26-03-1958| beer|\n", + "| 17| marie| curie| 17.7688| 912| 20|22-03-2000| rice|\n", + "| 18| arthur| compton|118.7527| 812| 100|01-01-1899| null|\n", + "| 19| james|chadwick| 96.7688| 467| 200|03-05-1921| null|\n", + "+---+------------+--------+--------+---------+-----+----------+----------+\n", "\n" ] } @@ -3418,55 +3188,53 @@ "name": "stdout", "output_type": "stream", "text": [ - "+----+--------------------+--------------------+---------+----------+-----+----------+--------+\n", - "| id| firstName| lastName|billingId| product|price| birth|dummyCol|\n", - "+----+--------------------+--------------------+---------+----------+-----+----------+--------+\n", - "| 1| Luis| Alvarez$$%!| 123| Cake| 10|1980/07/07| never|\n", - "| 2| André| Ampère| 423| piza| 8|1950/07/08| gonna|\n", - "| 3| NiELS| Böhr//((%%| 551| pizza| 8|1990/07/09| give|\n", - "| 4| PAUL| dirac$| 521| pizza| 8|1954/07/10| you|\n", - "|null| Albert| Einstein| 634| pizza| 8|1990/07/11| up|\n", - "| 6| Galileo| GALiLEI| 672| arepa| 5|1930/08/12| never|\n", - "| 7| CaRL| Ga%%%uss| 323| taco| 3|1970/07/13| gonna|\n", - "|null| David| H$$$ilbert| 624| taaaccoo| 3|1950/07/14| let|\n", - "| 9| Johannes| KEPLER| 735| taco| 3|1920/04/22| you|\n", - "| 10| JaMES| M$$ax%%well| 875| taco| 3|1923/03/12| down|\n", - "|null| Isaac| Newton| 992| pasta| 9|1999/02/15| never |\n", - "| 12| Emmy%%| Nöether$| 234| pasta| 9|1993/12/08| gonna|\n", - "| 13| Max!!!| Planck!!!| 111|hamburguer| 4|1994/01/04| run |\n", - "| 14| Fred| Hoy&&&le| 553| pizzza| 8|1997/06/27| around|\n", - "| 15|((( Heinrich )))))| Hertz| 116| pizza| 8|1956/11/30| and|\n", - "| 16| William| Gilbert###| 886| BEER| 2|1958/03/26| desert|\n", - "| 17| Marie| CURIE| 912| Rice| 1|2000/03/22| you|\n", - "| 18| Arthur| COM%%%pton| 812| 110790| 5|1899/01/01| #|\n", - "| 19| JAMES| Chadwick| 467| null| 10|1921/05/03| #|\n", - "| 19| JAMES| Chadwick| 467| null| 10|1921/05/03| #|\n", - "+----+--------------------+--------------------+---------+----------+-----+----------+--------+\n", + "+---+--------------------+--------------------+---------+----------+-----+----------+--------+\n", + "| id| firstName| lastName|billingId| product|price| birth|dummyCol|\n", + "+---+--------------------+--------------------+---------+----------+-----+----------+--------+\n", + "| 1| Luis| Alvarez$$%!| 123| Cake| 10|1980/07/07| never|\n", + "| 2| André| Ampère| 423| piza| 8|1950/07/08| gonna|\n", + "| 3| NiELS| Böhr//((%%| 551| pizza| 8|1990/07/09| give|\n", + "| 4| PAUL| dirac$| 521| pizza| 8|1954/07/10| you|\n", + "| 5| Albert| Einstein| 634| pizza| 8|1990/07/11| up|\n", + "| 6| Galileo| GALiLEI| 672| arepa| 5|1930/08/12| never|\n", + "| 7| CaRL| Ga%%%uss| 323| taco| 3|1970/07/13| gonna|\n", + "| 8| David| H$$$ilbert| 624| taaaccoo| 3|1950/07/14| let|\n", + "| 9| Johannes| KEPLER| 735| taco| 3|1920/04/22| you|\n", + "| 10| JaMES| M$$ax%%well| 875| taco| 3|1923/03/12| down|\n", + "| 11| Isaac| Newton| 992| pasta| 9|1999/02/15| never |\n", + "| 12| Emmy%%| Nöether$| 234| pasta| 9|1993/12/08| gonna|\n", + "| 13| Max!!!| Planck!!!| 111|hamburguer| 4|1994/01/04| run |\n", + "| 14| Fred| Hoy&&&le| 553| pizzza| 8|1997/06/27| around|\n", + "| 15|((( Heinrich )))))| Hertz| 116| pizza| 8|1956/11/30| and|\n", + "| 16| William| Gilbert###| 886| BEER| 2|1958/03/26| desert|\n", + "| 17| Marie| CURIE| 912| Rice| 1|2000/03/22| you|\n", + "| 18| Arthur| COM%%%pton| 812| 110790| 5|1899/01/01| #|\n", + "| 19| JAMES| Chadwick| 467| null| 10|1921/05/03| #|\n", + "+---+--------------------+--------------------+---------+----------+-----+----------+--------+\n", "\n", - "+----+------------+--------+---------+-----+----------+---------+----------+\n", - "| id| firstName|lastName|billingId|price| birth|clientAge| product|\n", - "+----+------------+--------+---------+-----+----------+---------+----------+\n", - "| 1| luis| alvarez| 123| 10|07-07-1980| 37.7177| cake|\n", - "| 2| andre| ampere| 423| 8|08-07-1950| 67.7124| pizza|\n", - "| 3| niels| bohr| 551| 8|09-07-1990| 27.7151| pizza|\n", - "| 4| paul| dirac| 521| 8|10-07-1954| 63.7258| pizza|\n", - "|null| albert|einstein| 634| 8|11-07-1990| 27.7151| pizza|\n", - "| 6| galileo| galilei| 672| 5|12-08-1930| 87.7204| arepa|\n", - "| 7| carl| gauss| 323| 3|13-07-1970| 47.7231| taco|\n", - "|null| david| hilbert| 624| 3|14-07-1950| 67.7124| taco|\n", - "| 9| johannes| kepler| 735| 3|22-04-1920| 97.7231| taco|\n", - "| 10| james| maxwell| 875| 3|12-03-1923| 94.7151| taco|\n", - "|null| isaac| newton| 992| 9|15-02-1999| 18.7258| pasta|\n", - "| 12| emmy| noether| 234| 9|08-12-1993| 24.7258| pasta|\n", - "| 13| max| planck| 111| 4|04-01-1994| 23.7285|hamburguer|\n", - "| 14| fred| hoyle| 553| 8|27-06-1997| 20.7204| pizza|\n", - "| 15| heinrich | hertz| 116| 8|30-11-1956| 61.7124| pizza|\n", - "| 16| william| gilbert| 886| 2|26-03-1958| 59.7204| beer|\n", - "| 17| marie| curie| 912| 1|22-03-2000| 17.7285| rice|\n", - "| 18| arthur| compton| 812| 5|01-01-1899| 118.7124| null|\n", - "| 19| james|chadwick| 467| 10|03-05-1921| 96.7285| null|\n", - "| 19| james|chadwick| 467| 10|03-05-1921| 96.7285| null|\n", - "+----+------------+--------+---------+-----+----------+---------+----------+\n", + "+---+------------+--------+---------+-----+----------+---------+----------+\n", + "| id| firstName|lastName|billingId|price| birth|clientAge| product|\n", + "+---+------------+--------+---------+-----+----------+---------+----------+\n", + "| 1| luis| alvarez| 123| 10|07-07-1980| 37.7581| cake|\n", + "| 2| andre| ampere| 423| 8|08-07-1950| 67.7527| pizza|\n", + "| 3| niels| bohr| 551| 8|09-07-1990| 27.7554| pizza|\n", + "| 4| paul| dirac| 521| 8|10-07-1954| 63.7661| pizza|\n", + "| 5| albert|einstein| 634| 8|11-07-1990| 27.7554| pizza|\n", + "| 6| galileo| galilei| 672| 5|12-08-1930| 87.7608| arepa|\n", + "| 7| carl| gauss| 323| 3|13-07-1970| 47.7634| taco|\n", + "| 8| david| hilbert| 624| 3|14-07-1950| 67.7527| taco|\n", + "| 9| johannes| kepler| 735| 3|22-04-1920| 97.7634| taco|\n", + "| 10| james| maxwell| 875| 3|12-03-1923| 94.7554| taco|\n", + "| 11| isaac| newton| 992| 9|15-02-1999| 18.7661| pasta|\n", + "| 12| emmy| noether| 234| 9|08-12-1993| 24.7661| pasta|\n", + "| 13| max| planck| 111| 4|04-01-1994| 23.7688|hamburguer|\n", + "| 14| fred| hoyle| 553| 8|27-06-1997| 20.7608| pizza|\n", + "| 15| heinrich | hertz| 116| 8|30-11-1956| 61.7527| pizza|\n", + "| 16| william| gilbert| 886| 2|26-03-1958| 59.7608| beer|\n", + "| 17| marie| curie| 912| 1|22-03-2000| 17.7688| rice|\n", + "| 18| arthur| compton| 812| 5|01-01-1899| 118.7527| null|\n", + "| 19| james|chadwick| 467| 10|03-05-1921| 96.7688| null|\n", + "+---+------------+--------+---------+-----+----------+---------+----------+\n", "\n" ] } diff --git a/examples/Optimus_Outliers_Example.ipynb b/examples/Optimus_Outliers_Example.ipynb index c8c41f85e..6990311a2 100644 --- a/examples/Optimus_Outliers_Example.ipynb +++ b/examples/Optimus_Outliers_Example.ipynb @@ -83,9 +83,7 @@ ], "source": [ "# Import optimus\n", - "import optimus as op\n", - "# Import os for reading from local\n", - "import os" + "import optimus as op" ] }, { @@ -114,8 +112,7 @@ }, "outputs": [], "source": [ - "path = \"file:///\" + os.getcwd() + \"/outliers.csv\"\n", - "df = tools.read_dataset_csv(path, delimiter_mark=\",\", header=\"true\")" + "df = tools.read_csv(\"outliers.csv\", delimiter_mark=\",\", header=\"true\")" ] }, { @@ -154,7 +151,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "From a quick inspection of the dataframe we can guess that the 1000 in the column 'num' can be an outlier. You can perform a very intense search to see if it is actually and outlier, if you need something like that please check out [these articles and tutorials](http://www.datasciencecentral.com/profiles/blogs/11-articles-and-tutorials-about-outliers)" + "From a quick inspection of the dataframe we can guess that the 1000 in the column `num` can be an outlier. You can perform a very intense search to see if it is actually and outlier, if you need something like that please check out [these articles and tutorials](http://www.datasciencecentral.com/profiles/blogs/11-articles-and-tutorials-about-outliers)" ] }, { @@ -483,17 +480,17 @@ " \n", " \n", "\n", - "
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Quantile statistics

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General description
FeaturesName or Quantity
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Columns8
Rows19
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Quantile statistics

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\n", @@ -779,7 +776,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 6, diff --git a/optimus/df_analyzer.py b/optimus/df_analyzer.py index dc5d5f614..f26f9e7a9 100644 --- a/optimus/df_analyzer.py +++ b/optimus/df_analyzer.py @@ -300,8 +300,8 @@ def valid_col_check(f): # Verifying if there is a column with different datatypes: # Sum the first and the second number: sum_first_and_second = lambda lis: [lis[x] + lis[0] if x == 1 else lis[x] for x in - range(len(lis))][ - 1:] + range(len(lis))][ + 1:] # Check if the column has different datatypes: different_types = sum([1 if x == 0 else 0 for x in sum_first_and_second(f[1:])]) < 2 @@ -350,10 +350,8 @@ def valid_col_check(f): if plots: self.plot_hist( - df_one_col=df_col_analyzer.select(column), - hist_dict=hist_dict, + column=column, type_hist='categorical', - num_bars=num_bars, values_bar=values_bar) plt.show() elif string_qty < number_qty: @@ -363,11 +361,9 @@ def valid_col_check(f): if plots: # Create the general blog and the "subplots" i.e. the bars - hist_plot = self.plot_hist(df_col_analyzer.select(column), - hist_dict=hist_dict, - type_hist='numerical', - num_bars=num_bars, - values_bar=values_bar) + self.plot_hist(column=column, + type_hist='numerical', + values_bar=values_bar) plt.show() else: @@ -502,6 +498,7 @@ def set_data_frame(self, df): """ self._df = df + @property def get_data_frame(self): """This function return the dataframe of the class""" return self._df @@ -558,7 +555,7 @@ def column_analyze(self, column_list, plots=True, values_bar=True, print_type=Fa # Counting time1 = time.time() - df_col_analizer = self._df + df_col_analyzer = self._df # Column assignation: columns = [] @@ -566,7 +563,7 @@ def column_analyze(self, column_list, plots=True, values_bar=True, print_type=Fa # If column_list is a string, convert it in a list: if isinstance(column_list, str): if column_list == "*": - columns = df_col_analizer.columns + columns = df_col_analyzer.columns else: columns = [column_list] @@ -576,7 +573,8 @@ def column_analyze(self, column_list, plots=True, values_bar=True, print_type=Fa # Asserting if columns provided are in dataFrame: assert all( - col in df_col_analizer.columns for col in columns), 'Error: Columns or column does not exist in the dataFrame' + col in df_col_analyzer.columns for col in + columns), 'Error: Columns or column does not exist in the dataFrame' types = {"string": "ABC", "int": "#", "integer": "#", "float": "##.#", "double": "##.#", "bigint": "#"} @@ -584,11 +582,11 @@ def column_analyze(self, column_list, plots=True, values_bar=True, print_type=Fa invalid_cols = [] json_cols = [] - # In case first is not set, this will analize all columns + # In case first is not set, this will analyze all columns for column in columns: - # Calling function analize: + # Calling function analyze: inv_col, _, _, d = self._analyze( - df_col_analizer.select(column), + df_col_analyzer.select(column), column, row_number, plots, @@ -611,14 +609,13 @@ def column_analyze(self, column_list, plots=True, values_bar=True, print_type=Fa if print_all: return invalid_cols, json_cols - def plot_hist(self, df_one_col, hist_dict, type_hist, num_bars=20, values_bar=True): + def plot_hist(self, column, type_hist, values_bar=True): """ This function builds the histogram (bins) of an categorical column dataframe. Inputs: df_one_col: A dataFrame of one column. hist_dict: Python dictionary with histogram values type_hist: type of histogram to be generated, numerical or categorical - num_bars: Number of bars in histogram. values_bar: If values_bar is True, values of frequency are plotted over bars. Outputs: dictionary of the histogram generated ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -631,15 +628,19 @@ def plot_hist(self, df_one_col, hist_dict, type_hist, num_bars=20, values_bar=Tr assert type_hist == 'categorical' or ( type_hist == 'numerical'), "Error, type_hist only can be 'categorical' or 'numerical'." + df_one_col = self._df.select(column) + # Getting column of dataframe provided - column = df_one_col.columns[0] + column_plot = df_one_col.columns[0] if type_hist == 'categorical': # Plotting histogram - self._plot_cat_hist(hist_dict, column, values_bar=True) + hist_dict = self.get_categorical_hist(df_one_col, num_bars=10) + self._plot_cat_hist(hist_dict, column_plot, values_bar) else: # Plotting histogram - self._plot_num_hist(hist_dict, column, values_bar=True) + hist_dict = self.get_numerical_hist(df_one_col, num_bars=10) + self._plot_num_hist(hist_dict, column_plot, values_bar) def get_categorical_hist(self, df_one_col, num_bars): """This function analyzes a dataframe of a single column (only string type columns) and @@ -682,7 +683,7 @@ def get_numerical_hist(self, df_one_col, num_bars): # If we obtain a null column: assert not isinstance(temp_df.first(), type(None)), \ "Error, Make sure column dataframe has numerical features. One of the first actions \ - getNumericalHist function does is a convertion dataType from original datatype \ + getNumericalHist function does is a change dataType from original datatype \ to float. If the column provided has only values that are \ not numbers parseables to float, it will flag this error." @@ -705,68 +706,69 @@ def get_numerical_hist(self, df_one_col, num_bars): # Si la cantidad de bins es menor que los valores unicos, entonces se toman los valores unicos como bin. if len(bins_values) < len(uni_values): + bins_values = uni_values - # This function search over columnName dataFrame to which interval belongs each cell - # It returns the columnName dataFrame with an additional columnName which describes intervals of each columnName cell. - def generate_expr(column_name, list_intervals): - if len(list_intervals) == 1: - return when(col(column_name).between(list_intervals[0][0], list_intervals[0][1]), 0).otherwise(None) - else: - return (when((col(column_name) >= list_intervals[0][0]) & (col(column_name) < list_intervals[0][1]), - len(list_intervals) - 1) - .otherwise(generate_expr(column_name, list_intervals[1:]))) - - # +--------+--------------------+ - # |columns |Number of list pairs| - # +--------+--------------------+ - # | 5| 4| - # | 3| 7| - # | 6| 3| - # | 9| 0| - # | 1| 9| - # | 6| 3| - # | 4| 6| - # +--------+--------------------+ - - # Getting ranges from list: i.e. [(0,1),(1,2),(2,3),(3,4)] - func_pairs = lambda liste: [(liste[x], liste[x + 1]) for x in range(0, len(liste) - 1)] - ranges = func_pairs(bins_values) - - # Identifying to which group belongs each cell of column Dataframe and count them in order to get frequencies - # for each searh interval. - freq_df = temp_df.select(col(column), generate_expr(column, ranges).alias("value")) \ - .groupBy("value").count() - - # +-----------+-----+ - # |intervals |count| - # +-----------+-----+ - # | 0| 2| - # | 1| 3| - # | 4| 2| - # | 5| 1| - # | 6| 1| - # | 10| 1| - # +-----------+-----+ - - # Reverting the order of the list ranges, because 0 in the last interval in list provided to get FreqDF - # so it is more intuitive if the list of ranges is reverted. Then the first and second pair interval in ranges1 - # correspond to 0 and 1 interval in list - ranges1 = list(reversed(ranges)) - - # From intervals, bins are calculated as the average of min and max interval. - bins = [np.mean([rmin, rmax]) for (rmin, rmax) in ranges1] - - func = udf(lambda x: float(bins[x]), DoubleType()) - - # Setting position of bars according to bins and group intervals: - freq_df = freq_df.na.drop().withColumn('value', func(col('value'))) - - # Extracting information dataframe into a python dictionary: - hist_dict = [] - for row in freq_df.collect(): - hist_dict.append(self._create_dict(['value', 'cont'], [row[0], row[1]])) - - return hist_dict + # This function search over columnName dataFrame to which interval belongs each cell + # It returns the columnName dataFrame with an additional columnName which describes intervals of each columnName cell. + def generate_expr(column_name, list_intervals): + if len(list_intervals) == 1: + return when(col(column_name).between(list_intervals[0][0], list_intervals[0][1]), 0).otherwise(None) + else: + return (when((col(column_name) >= list_intervals[0][0]) & (col(column_name) < list_intervals[0][1]), + len(list_intervals) - 1) + .otherwise(generate_expr(column_name, list_intervals[1:]))) + + # +--------+--------------------+ + # |columns |Number of list pairs| + # +--------+--------------------+ + # | 5| 4| + # | 3| 7| + # | 6| 3| + # | 9| 0| + # | 1| 9| + # | 6| 3| + # | 4| 6| + # +--------+--------------------+ + + # Getting ranges from list: i.e. [(0,1),(1,2),(2,3),(3,4)] + func_pairs = lambda liste: [(liste[x], liste[x + 1]) for x in range(0, len(liste) - 1)] + ranges = func_pairs(bins_values) + + # Identifying to which group belongs each cell of column Dataframe and count them in order to get frequencies + # for each searh interval. + freq_df = temp_df.select(col(column), generate_expr(column, ranges).alias("value")) \ + .groupBy("value").count() + + # +-----------+-----+ + # |intervals |count| + # +-----------+-----+ + # | 0| 2| + # | 1| 3| + # | 4| 2| + # | 5| 1| + # | 6| 1| + # | 10| 1| + # +-----------+-----+ + + # Reverting the order of the list ranges, because 0 in the last interval in list provided to get FreqDF + # so it is more intuitive if the list of ranges is reverted. Then the first and second pair interval in ranges1 + # correspond to 0 and 1 interval in list + ranges1 = list(reversed(ranges)) + + # From intervals, bins are calculated as the average of min and max interval. + bins = [np.mean([rmin, rmax]) for (rmin, rmax) in ranges1] + + func = udf(lambda x: float(bins[x]), DoubleType()) + + # Setting position of bars according to bins and group intervals: + freq_df = freq_df.na.drop().withColumn('value', func(col('value'))) + + # Extracting information dataframe into a python dictionary: + hist_dict = [] + for row in freq_df.collect(): + hist_dict.append(self._create_dict(['value', 'cont'], [row[0], row[1]])) + + return hist_dict def unique_values_col(self, column): """This function counts the number of values that are unique and also the total number of values. @@ -798,11 +800,11 @@ def correlation(self, vec_col, method="pearson"): assert isinstance(method, str), "Error, method argument provided must be a string." assert method == 'pearson' or ( - method == 'spearman'), "Error, method only can be 'pearson' or 'sepearman'." + method == 'spearman'), "Error, method only can be 'pearson' or 'sepearman'." cor = Correlation.corr(self._df, vec_col, method).head()[0].toArray() return sns.heatmap(cor, mask=np.zeros_like(cor, dtype=np.bool), cmap=sns.diverging_palette(220, 10, - as_cmap=True)) + as_cmap=True)) @classmethod def write_json(cls, json_cols, path_to_json_file): @@ -825,3 +827,50 @@ def display_optimus(cls, df): # Import pixiedust-optimus from pixiedust_optimus.display import display display(df) + + def get_frequency(self, columns, sort_by_count=True): + """ + Get frequencies for values inside columns. + + :param columns: String or List of columns to analyze + :param sort_by_count: Boolean if true the counts will be sort desc. + :return: Dataframe with counts per existing values in each column. + """ + # Asserting data variable is string or list: + assert isinstance(columns, (str, list)), "Error: Column argument must be a string or a list." + + # If None or [] is provided with column parameter: + assert columns != [], "Error: Column can not be a empty list []" + + # Columns + if isinstance(columns, str): + columns = [columns] + + # Columns + assert all(col in self._df.columns for col in columns), \ + 'Error: Columns or column does not exist in the dataFrame' + + def frequency(columns, sort_by_count): + if sort_by_count: + for column in columns: + freq = (self._df + .groupBy(column) + .count() + .orderBy("count", ascending=False)) + + if freq.where("count > 1").count() == 0: + print("No values to group in column", column) + else: + freq.show() + else: + for column in columns: + freq = (self._df + .groupBy(column) + .count()) + + if freq.where("count > 1").count() == 0: + print("No values to group in column", column) + else: + freq.show() + + return frequency(columns, sort_by_count) diff --git a/optimus/df_transformer.py b/optimus/df_transformer.py index da3207e43..7f1d8f963 100644 --- a/optimus/df_transformer.py +++ b/optimus/df_transformer.py @@ -684,14 +684,16 @@ def move_col(self, column, ref_col, position): def count_items(self, col_id, col_search, new_col_feature, search_string): """ - This function can be used to split a feature with some extra information in order - to make a new column feature. + This function can be used to create Spark DataFrames with frequencies for picked values of + selected columns. :param col_id column name of the columnId of dataFrame :param col_search column name of the column to be split. :param new_col_feature Name of the new column. :param search_string string of value to be counted. + :returns Spark Dataframe. + Please, see documentation for more explanations about this method. """ @@ -733,8 +735,8 @@ def count_items(self, col_id, col_search, new_col_feature, search_string): df_mod = subdf.join(new_column, exprs, 'left_outer') # Cleaning dataframe: - df_mod = df_mod.drop(col_id + '_other').drop(col_search).withColumnRenamed('count', new_col_feature)\ - .dropna("any") + df_mod = df_mod.drop(col_id + '_other').drop(col_search).withColumnRenamed('count', new_col_feature) \ + .dropna("any") print("Counting existing "+search_string + " in "+col_search) return df_mod.sort(col_id).drop_duplicates([col_id]) diff --git a/optimus/utilities.py b/optimus/utilities.py index dddc64413..c9ecd0012 100644 --- a/optimus/utilities.py +++ b/optimus/utilities.py @@ -26,7 +26,7 @@ def __init__(self): # Set empty container for url self.url = "" - def read_dataset_csv(self, path, delimiter_mark=';', header='true'): + def read_csv(self, path, delimiter_mark=';', header='true'): """This funcion read a dataset from a csv file. :param path Path or location of the file. @@ -124,7 +124,7 @@ def csv_to_parquet(self, input_path, output_path, delimiter_mark_csv, header_csv :param header_csv This argument specifies if csv file has header or not. :param num_partitions Specifies the number of partitions the user wants to write the dataset.""" - df = self.read_dataset_csv(input_path, delimiter_mark_csv, header=header_csv) + df = self.read_csv(input_path, delimiter_mark_csv, header=header_csv) if num_partitions is not None: assert (num_partitions <= df.rdd.getNumPartitions()), "Error: num_partitions specified is greater that the" \ diff --git a/optimus/version.py b/optimus/version.py index 7d8e89a6e..78f1b44b1 100644 --- a/optimus/version.py +++ b/optimus/version.py @@ -4,5 +4,5 @@ def _safe_int(string): except ValueError: return string -__version__ = '1.0.2' +__version__ = '1.0.3' VERSION = tuple(_safe_int(x) for x in __version__.split('.')) diff --git a/setup.py b/setup.py index 96d226e82..541fed6a1 100644 --- a/setup.py +++ b/setup.py @@ -36,7 +36,7 @@ def readme(): author='Favio Vazquez', author_email='favio.vazquez@ironmussa.com', url='https://github.com/ironmussa/Optimus/', - download_url='https://github.com/ironmussa/Optimus/archive/1.0.2.tar.gz', + download_url='https://github.com/ironmussa/Optimus/archive/1.0.3.tar.gz', description=('Optimus is the missing framework for cleaning and preprocessing data in a distributed fashion with ' 'pyspark.'), long_description=readme(), diff --git a/tests/tests.py b/tests/tests.py index af3d808c4..3d96f18bb 100644 --- a/tests/tests.py +++ b/tests/tests.py @@ -55,6 +55,27 @@ def create_other_df(spark_session): sys.exit(1) +def create_another_df(spark_session): + try: + # Building a simple dataframe: + schema = StructType([ + StructField("city", StringType(), True), + StructField("dates", StringType(), True), + StructField("population", IntegerType(), True)]) + + dates = ['1991/02/25', '1998/05/10', '1993/03/15', '1992/07/17'] + cities = ['Caracas', 'Caracas', ' Maracaibo ', 'Madrid'] + population = [37800000, 19795791, 12341418, 6489162] + + # Dataframe: + df = spark_session.createDataFrame(list(zip(cities, dates, population)), schema=schema) + assert_spark_df(df) + return df + except RuntimeError: + logger.exception('Could not create other dataframe.') + sys.exit(1) + + def test_transformer(spark_session): try: transformer = op.DataFrameTransformer(create_df(spark_session)) @@ -197,3 +218,13 @@ def test_date_transform(spark_session): except RuntimeError: logger.exception('Could not run date_transform().') sys.exit(1) + + +def test_get_frequency(spark_session): + try: + analyzer = op.DataFrameAnalyzer(create_another_df(spark_session)) + analyzer.get_frequency(columns="city", sort_by_count=True) + assert_spark_df(analyzer.get_data_frame) + except RuntimeError: + logger.exception('Could not run get_frequency().') + sys.exit(1)