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hw4/etl.py 0 → 100644
'''
The etl module defines basic language primitives for manipulating Pandas
DataFrames. It takes a DataFrame in and outputs a transformed DataFrame.
You will implement several of the routines to perform these transformations.
Example Usage:
You can create DataFrame and create an ETL class that takes the DataFrame
as input.
>> df1 = pd.DataFrame([['Bob', 'Stewart'],
['Anna', 'Davis'],
['Jerry', 'Dole'],
['John', 'Marsh']],
columns=["first_name", "last_name"])
>> etl = ETL(df1)
The add() function creates a new column with a specified value:
>> etl.add(colname="age", 0)
>> pw1.df
first_name last_name age
0 Bob Stewart 0
1 Anna Davis 0
2 Jerry Dole 0
3 John Marsh 0
'''
import pandas as pd
import re
class ETL:
'''
The class that defines ETL transformations for a single dataframe
'''
def __init__(self, df: pd.DataFrame):
'''
ETL objects are constructed with a source
dataframe. These dataframes are manipulated
in-place.
'''
#how to access the source dataframe
self.df = df
#stores a history of the transformations to the df
self.transforms = []
def add(self, colname, x):
'''
The add(colname, x) function adds a column with the specified name
(colname) and a specified value (x). It adds this value to
all rows of the dataframe. We've implemented this as an
example to show you how to structure your ETL functions.
add *modifies* self.df as well as *returns* it
'''
#Test to see if colname is None, if so use a default colname
self.df[colname] = x
#append your changes to the transform list
self.transforms.append(self.df.copy(deep=True))
return self.df
def drop(self, colname):
'''
The drop(colname) function returns a DataFrame
with the column (colname) removed.
drop *modifies* self.df as well as *returns* it
'''
#YOUR CODE HERE
self.transforms.append(self.df.copy(deep=True))
return self.df
def copy(self, colname, new_colname):
'''
copy(colname, new_colname) duplicates a column and
saves it to the new_colname.
copy *modifies* self.df as well as *returns* it.
'''
#YOUR CODE HERE
self.transforms.append(self.df.copy(deep=True))
return self.df
def split(self, colname, new_colname, splitter):
'''
split(colname, new_colname, splitter) takes a column
splits the value on a delimiter. It replaces colname
with the substrings that appear before the delimiter
and puts the values after the delimiter in the
new_colname. If the string does not contain the delimiter
then new_colname is assigned an empty string.
split *modifies* self.df as well as *returns* it.
'''
#YOUR CODE HERE
self.transforms.append(self.df.copy(deep=True))
return self.df
def merge(self, col1, col2, splitter):
'''
merge(col1, col2, splitter) replaces col1
with the values of col1 and col2 concatenated,
and seperated by the delimiter. The delimiter is
ignored if either df.col1 or df.col2 is an empty
string.
merge *modifies* self.df as well as *returns* it.
'''
#YOUR CODE HERE
self.transforms.append(self.df.copy(deep=True))
return self.df
def format(self, colname, fn):
'''
format applies an input function to every value in colname. fn
is a *function*.
format *modifies* self.df as well as *returns* it.
'''
#YOUR CODE HERE
self.transforms.append(self.df.copy(deep=True))
return self.df
def divide(self, colname, new_colname1, new_colname2, condition):
'''
Divide conditionally divides a column, sending values that
satisfy the condition into one of two columns
(new_colname1 or new_colname2). condition is a Boolean function
of values.
See examples in the writeup.
'''
#YOUR CODE HERE
self.transforms.append(self.df.copy(deep=True))
return self.df
hw4/etl_programs.py 0 → 100644
'''
Here, you will write programs that transform dataframes
using the functions that you wrote.
'''
def phone():
'''
Write an ETL program that results in a
dataframe with two columns: area_code, phone_number.
'''
df = pd.DataFrame([['(408)996-758'],
['+1 667 798 0304'],
['(774)998-758'],
['+1 442 030 9595']],
columns=["phoneno"])
etl = ETL(df)
#Your code goes here
return etl.df
def date():
'''
Write an ETL program that results in a
dataframe with three columns: day, month, year.
The day must be in two-digit format i.e, 01, 08.
The month must be the full month name, e.g., "May".
The year must be in YYYY format.
'''
df = pd.DataFrame([['03/2/1990'],
['2/14/1964'],
['1990-04-30'],
['7/9/2012'],
['1989-09-13'],
['1994-08-21'],
['1996-11-30'],
['2004-12-23'],
['4/21/2016']]
columns=["date"])
etl = ETL(df)
#Your code goes here
return etl.df
def name():
'''
Write an ETL program that correctly formats names
into first_name and last_name.
'''
df = pd.DataFrame([['Such,Bob', ''],
['Ann', 'Davis'],
['Dole,Jerry', ''],
['Joan', 'Song']],
columns=["first_name", "last_name"])
etl = ETL(df)
#Your code goes here
return etl.df
\ No newline at end of file
hw5/README.md 0 → 100644
# Out-of-Core Group By Aggregate
*Due 6/1/19 11:59 PM*
In this assignment, you will implement an out-of-core
version of the group by aggregate (aggregation by key)
seen in lecture. You will have a set memory limit and
you will have to count the number of times a string shows
up in an iterator. Your program should work for any limit
> 20.
## Getting Started
First, pull the most recent changes from the cmsc13600-public repository:
```
$ git pull
```
Then, copy the `hw5` folder to your submission repository. Change directories to enter your submission repository. Your code will go into `countD.py` this is the only file that you will modify. Finally, add `countD.py` using `git add`:
```
$ git add countD.py
$ git commit -m'initialized homework'
```
Now, you will need to fetch the data used in this assignment. Download title.csv put it in the hw5 folder:
https://www.dropbox.com/s/zl7yt8cl0lvajxg/title.csv?dl=0
DO NOT ADD title.csv to the git repo! After downloading the
dataset, there is a python module provided for you called `core.py`, which reads the dataset. This module loads the data in as
an iterator in two functions `imdb_years()` and `imdb_title_words()`:
```
>> for i in imdb_years():
... print(i)
1992
1986
<so on>
```
Play around with both `imdb_years()` and `imdb_title_words()` to get a feel for how the data works.
## MemoryLimitedHashMap
In this project, the main data structure is the `MemoryLimitedHashMap`. This is a hash map that has an explicit limit on the number of keys it can store. To create one of these data structure, you can import it from core module:
```
from core import *
#create a memory limited hash map
m = MemoryLimitedHashMap()
```
To find out what the limit of this hash map is, you can:
```
print("The max size of m is: ", m.limit)
```
The data structure can be constructed with an explicit limit (the default is 1000), e.g., `MemoryLimitedHashMap(limit=10)`.
Adding data to this hash map is like you've probably seen before in a data structure class. There is a `put` function that takes in a key and assigns that key a value:
```
# put some keys
m.put('a', 1)
m.put('b', 45)
print("The size of m is: ", m.size())
```
You can fetch the data using the `get` function and `keys` function:
```
# get keys
for k in m.keys():
print("The value at key=", k, 'is', m.get(k))
# You can test to see if a key exists
print('Does m contain a?', m.contains('a'))
print('Does m contain c?', m.contains('c'))
```
When a key does not exist in the data structure the `get` function will raise an error:
```
#This gives an error:
m.get('c')
```
Similarly, if you assign too many unique keys (more than the limit) you will get an error:
```
for i in range(0,1001):
m.put(str(i), i)
```
The `MemoryLimitedHashMap` allows you to manage this limited storage with a `flush` function that allows you to persist a key and its assignment to disk. When you flush a key it removes it from the data structure and decrements the limit. Flush takes a key as a parameter.
```
m.flushKey('a')
print("The size of m is: ", m.size())
```
Note that the disk is not intelligent! If you flush a key multiple times it simply appends the flushed value to a file on disk:
```
m.flushKey('a')
<some work...>
m.flushKey('a')
```
Once a key has been flushed it can be read back using the `load` function (which takes a key as a parameter). This loads back *all* of the flushed values:
```
#You can also load values from disk
for k,v in m.load('a'):
print(k,v)
```
If you try to load a key that has not been flushed, you will get an error:
```
#Error!!
for k,v in m.load('d'):
print(k,v)
```
If you want multiple flushes of the same key to be differentiated, you can set a *subkey*:
```
#first flush
m.flushKey('a', '0')
<some work...>
#second flush
m.flushKey('a', '1')
```
The `load` function allows you to selectively pull
certain subkeys:
```
# pull only the first flush
m.load('a', '0')
```
We can similarly iterate over all of the flushed data (which optionally takes a subkey as well!):
```
for k,v in m.loadAll():
print(k,v)
```
There is also a way to iterate over all of the flushed keys (will strip out subkeys):
```
m.fKeys()
```
## Count Per Group
In this assignment, you will implement an out-of-core count operator which for all distinct strings in an iterator returns
the number of times it appears (in no particular order).
For example,
```
In: "the", "cow", "jumped", "over", "the", "moon"
Out: ("the",2), ("cow",1), ("jumped",1), ("over",1), ("moon",1)
```
Or,
```
In: "a", "b", "b", "a", "c"
Out: ("c",1),("b",2), ("a", 2)
```
The catch is that you CANNOT use a list, dictionary, or set from
Python. We provide a general purpose data structure called a MemoryLimitedHashMap (see ooc.py). You must maintain the iterator
state using this data structure.
The class that you will implement is called Count (in countD.py).
The constructor is written for you, and ittakes in an input iterator and a MemoryLimitedHashMap. You will use these objects
in your implementation. You will have to implement `__next__` and `__iter__`. Any solution using a list, dictionary, or set inside `Count` will recieve 0 points.
The hint is to do this in multiple passes and use a subkey to track keys flushed between different passes.
## Testing and Submission
We have provided a series of basic tests in `auto_grader.py`, these tests are incomplete and are not meant to comprehensively grade your assignment. There is a file `years.json` with an expected output. After you finish the assignment you can submit your code with:
```
$ git push
```
hw5/auto_grader.py 0 → 100644
from countD import *
from core import *
from ooc import *
import json
test_file = open('years.json','r')
expected = json.loads(test_file.read())
for l in range(80, 140, 20):
m = MemoryLimitedHashMap(limit = l)
actual = {k:v for k,v in Count(imdb_years(), m)}
print("Memory Limit", l, expected == actual)
hw5/core.py 0 → 100644
'''
The core module sets up the data structures and
and references for this programming assignment.
'''
import platform
import os
import json
if platform.system() == 'Windows':
print("This assignment will not work on a windows computer")
exit()
def imdb_title_words():
f = open('title.csv','r')
line = f.readline()
while line != "":
words = line.strip().split(',')[1].split()
for w in words:
yield w
line = f.readline()
f.close()
def imdb_years():
f = open('title.csv','r')
line = f.readline()
while line != "":
csvsplit = line.strip().split(',')
year = csvsplit[len(csvsplit) - 8]
if year.strip() != "":
yield year
line = f.readline()
f.close()
hw5/countD.py 0 → 100644
"""
Get the dataset first, download title.csv put it in the pa1 folder
https://www.dropbox.com/s/zl7yt8cl0lvajxg/title.csv?dl=0
Count the number of times each symbol shows up in an iterator
with limited memory. Your program should work for any limit
> 20.
"""
class Count:
"""
In this assignment, you will implement an out-of-core count
operator which for all distinct strings in an iterator returns
the number of times it appears (in no particular order).
For example,
In: "the", "cow", "jumped", "over", "the", "moon"
Out: ("the",2), ("cow",1), ("jumped",1), ("over",1), ("moon",1)
Or,
In: "a", "b", "b", "a", "c"
Out: ("c",1),("b",2), ("a", 2)
The catch is that you CANNOT use a list, dictionary, or set from
python. We provide a general purpose data structure called a
MemoryLimitedHashMap (see ooc.py). You must maintain the iterator
state using this data structure.
"""
def __init__(self, input, memory_limit_hashmap):
'''
The constructor takes in an input iterator and
a MemoryLimitedHashMap. You will use these objects
in your implementation.
'''
self.in1 = input
self.hashmap = memory_limit_hashmap
def __iter__(self):
raise NotImplemented("You must implement an initializer")
def __next__(self):
raise NotImplemented("You must implement a next function")
hw5/ooc.py 0 → 100644
import os
import json
class MemoryLimitedHashMap(object):
'''
A MemoryLimitedHashMap simulates a hardware memory limit for a
key-value data structure. It will raise an exception if the
limit is exceeded.
Keys must be strings
'''
def __init__(self, diskfile='disk.file', limit=1000):
'''
The constructor takes a reference to a persistent file
and a memory limit.
'''
if os.path.exists(diskfile):
print("[Warning] Overwriting the Disk File", diskfile)
import shutil
shutil.rmtree(diskfile)
os.mkdir(diskfile)
self.diskfile = diskfile
self._data = {}
self.limit = limit
def size(self):
return len(self._data)
def put(self, k, v):
'''
Basically works like dict put
'''
if not self.contains(k) and len(self._data) == self.limit:
raise ValueError("[Error] Attempting to Insert Into a Full Map: " + str((k,v)))
else:
self._data[k] = v
def get(self, k):
'''
Basically works like dict get
'''
return self._data[k]
def contains(self, k):
'''
Basically works like hash map contains
'''
return (k in self._data)
def keys(self):
'''
Returns a set of keys. Tuple
is (key, location)
'''
return set([k for k in self._data])
def fKeys(self):
'''
Returns a set over keys that have been flushed.
Tuple is (key, location)
'''
return set([self.path2Key(k) for k in os.listdir(self.diskfile)])
def keyPath(self, k, subkey):
return self.diskfile+"/"+str(k)+ "_" + subkey
def path2Key(self, k):
key = k.split("_")[0]
return key
def flushKey(self, k, subkey):
'''
Removes the key from the dictionary and
persists it to disk.
'''
if not self.contains(k):
raise ValueError("[Error] Map Does Not Contain " + k)
f = open(self.keyPath(k, subkey), 'a')
f.write(json.dumps(self.get(k)) + "\n")
f.close()
del self._data[k] #free up the space
def load(self, k, subkey=""):
'''
Streams all of the data from a persisted key
'''
fname = self.keyPath(k, subkey)
if not os.path.exists(fname):
raise ValueError("[Error] Disk Does Not Contain " + k)
f = open(fname, 'r')
line = f.readline()
while line != "":
yield (k, json.loads(line.strip()))
line = f.readline()
def loadAll(self, subkey=""):
'''
Streams all of the data from all keys
'''
for k in self.keys():
yield (k, self.get(k))
for k in self.fKeys():
for _,v in self.load(k, subkey):
yield (k,v)
\ No newline at end of file
hw5/years.json 0 → 100644
{"1944": 1831, "1917": 4514, "1907": 1487, "1975": 14054, "1880": 1, "2003": 67777, "2012": 164307, "1958": 9768, "1894": 93, "1904": 1136, "1979": 14926, "1915": 7670, "2010": 141703, "2017": 3, "1982": 14770, "1954": 7199, "2014": 3077, "1948": 2840, "1893": 2, "1923": 2614, "2005": 95005, "2009": 128696, "1992": 24917, "1949": 3847, "1945": 1730, "1896": 791, "1902": 1799, "1976": 13994, "1965": 13063, "1961": 11073, "1951": 5663, "1962": 10308, "1929": 2800, "2000": 53013, "1910": 4597, "1912": 7770, "1898": 1740, "1901": 1747, "1922": 3066, "1957": 9491, "1927": 2915, "2001": 58590, "1940": 2202, "2002": 62568, "1995": 36437, "1913": 8902, "1999": 50564, "1994": 30027, "1932": 2567, "1980": 14779, "1925": 2786, "1983": 15489, "1889": 2, "1973": 14284, "1936": 2798, "1892": 9, "1939": 2483, "1971": 14442, "1981": 14456, "2013": 63827, "1942": 2181, "1968": 14235, "1930": 2543, "1990": 23040, "2015": 401, "1914": 8125, "1909": 3417, "1906": 1104, "1920": 4012, "1972": 13623, "1891": 7, "1938": 2730, "1985": 18391, "1911": 5945, "1947": 2291, "1921": 3627, "1888": 5, "1963": 11153, "2016": 32, "1895": 120, "1964": 11416, "1977": 14038, "1984": 16571, "2008": 122861, "1997": 38955, "1955": 8007, "1900": 1816, "1989": 21312, "1966": 13711, "1967": 14601, "1950": 4763, "1918": 3781, "1890": 6, "1931": 2507, "1986": 19440, "1905": 800, "1919": 3613, "1960": 11121, "1899": 1787, "1943": 1960, "1969": 14349, "1974": 13736, "1988": 19861, "2007": 119565, "1935": 2467, "1959": 10517, "1903": 2618, "1926": 2847, "1916": 5835, "1978": 14428, "1937": 2795, "1993": 26775, "1908": 2712, "2004": 84593, "1996": 36509, "1924": 2615, "2006": 108429, "1953": 6834, "1952": 6346, "2019": 2, "1941": 2206, "1970": 15000, "1987": 20122, "2011": 160017, "1934": 2493, "1991": 23799, "1946": 1965, "1998": 46583, "1933": 2433, "1897": 1309, "1956": 8628, "1928": 2773}
\ No newline at end of file
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