Squirrel Module

Table of Content

Introduction
Installation
Setting Up
Documentary
Feature List
Examples

Introduction

Squirrel is the best way to work with 2 dimensional lists. It is a lightweight alternative for pandas. Pandas is a fully functional module that supports lots of data visualisation module. But, if you want to use pandas in your project and if you planning to publish your project as an executable file, pandas needs lots of disk space. I realized it when I finished one of my project and I was proud of myself... So I had to reprogram it, with my own module. Also Squirrel can be used as a database module.

You can still use squirrel instead of pandas on your data science projects if you want to reduce the app file size.

Installation

Fow now, before I upload it to PyPi, you can download squirrel.py and move to your workspace and import with command below in the Setting Up section.

Setting Up

Write the command below to import squirrel in your worksheet.

import squirrel as sq
from squirrel import inf,pi,e

Documentary

There is 4 basic data storage class. All of them supports 2 dimensional list methods. They are:

ValueBase
StrBase
FloatBase
IntBase

ValueBase determines which data type entered itself.

StrBase automatically converts entered data to string.

FloatBase automatically converts entered data to float, pi, e or infinite.

IntBase automatically converts entered data to to integer or infinite.

If any data cannot convert to specified data type, it returns None.

IntBase, FloatBase, StrBase classes inherits ValueBase class's features.

IntBase and FloatBase classes has more methods than other classes. That methods provides lots of math function.

Feature List

Features

Basic Features

Feature	Example
getitem, setitem	Allows you get and set specified row.
add	Adds a row.
remove	Removes a single cell in a row.
removeRow	Removes a whole row.
addMany	Adds multiple row in a time.
indexof	Finds index number of a given row.
sort	Sorts data by specified column.
replace	Replaces old values with new specified amount.
fillNull	Fills null places with None.
fillNone	Fills None (and nulls) with specified value.
csv support	Able to read, add and write csv files.
data	Returns a copy of data as 2D lists.
apply	Apply a function that return new values.
applyMany	Apply more functions at the same time.
allColumns	Returns a range object zero to column count.
print	Prints all data in database as pretty display.
head	Prints first nth data.
tail	Prints last nth data.
section	Prints specified section of data.
columns	Returns all column names in a list.
allColumns	Returns a range object for all columns.
c	Returns integer value of column.
renameColumns	Renames columns.
range	Returns a range object by taking column names.

Math Features

Feature	Example
inf, pi, e	Yess infinite pie!
None for NA	None value used for NA.
median	Returns median value of specified column.
mean	Returns mean value of specified column.
mod	Returns mode values of specified column.
min	Returns min value of specified column.
max	Returns max value of specified column.
quartiles	Returns 1st, 3rt quartile of specified column.
abs	Converts values their absolute value.
oppositeSign	Multiply values on specified columns with -1.
increase	Increases all values on specified column.
decrease	Decreases all values on specified column.
multiply	Multiplies all values on specified column.
divide	Divides all values on specified column.
floorDivide	Floor divides all values on specified column.
modulus	Moduluses all values on specified column.
sum	Returns sums of all values on specified axis.
compare	Compares values and applies a query by math.
compareMany	Applies more than one compare.

Some Useful Features

Feature	Example
transpose	Transposes rows and columns.
mirror	Flips the database on the y-axis.
flip	flips the database on the x-axis.
rotate	Rotates database 90, 180 and 270 degrees.
removeDuplicates	Removes duplicate rows from database.
swap	Swaps two instances' positions.
swapRow	Swaps two rows' positions.
moveUp	Swaps item with upper one.
moveDown	Swaps item with lower one.
moveLeft	Swaps item with item on the left.
moveRight	Swaps item with item on the right.

Working With More Than One Database

Feature	Example
merge	Merges two database.
split	Returns same type of databases in a tuple.

DataBase Features

Feature	Example
count	Counts specified value.
query	Searchs values matched with query string.
multiple query	Applies multiple queries.
regex query	Searchs values matched with regex query.
multiple regex query	Applies multiple regex queries.
election	Just keep query results.
multiple election	Just keep multiple query result.
fetch	Gives search result as list objects.
fetch one	Gives the first search result as list object.
fetch by multi query	Gives multiple filter results as list objects.
fetch one by multi query	Gives multiple filter results as list objects.
clone	Gives a copy of database
cloneFromColumns	Gives specified columns of database.
cloneFromColumnRange	Gives specified column range of database.
truncate	Empties database.
saveDB	Saves database to a .sqr file.
loadDB	Loads a database from a .sqr file.
saveSecure	Saves database as base64 encrypted .sqs file.
loadSecure	Loads a database from a .sqs file.

Examples

ValueBase for Examples

db=sq.ValueBase(
    ["Elagoht",17],    # Any type of iterable will be accepted.
    {"Furkan":0,21:0}, # Dictionaries only gives keys.
    ("Baytekin","1"),  
    {10,20})           # Using sets does not make sense if you work with numeric and string values in same column.
db.print()

# Output
<ValueBase with 4 rows>
Items = [
 * [  Column 0  | Column 1 ]
 0 [ "Elagoht"  | 17       ]
 1 [ "Furkan"   | 21       ]
 2 [ "Baytekin" | "1"      ]
 3 [ 10         | 20       ]
]

RenameColumns -> None

db.renameColumns("Name","ID")
db.print()

# Output
<ValueBase with 4 rows>
Items = [
 * [    Name    |  ID ]
 0 [ "Elagoht"  | 17  ]
 1 [ "Furkan"   | 21  ]
 2 [ "Baytekin" | "1" ]
 3 [ 10         | 20  ]
]

# First column shows column names and it has stars to be separated from others.

C -> Int

print(db.c("Name"))

# Output 
0 

# Returns integer value of column. Works like "enumerate" item.

Query -> Valuebase | StrBase | IntBase | FloatBase

search=db.query("2",False,[db.c("ID")]) # This means search for "2" on only ID column.
search.print() # Query method returns a database, so you can use database methods to result object.

# Output
<ValueBase with 2 rows>
Items = [
 * [   Name   | ID ]
 0 [ "Furkan" | 21 ]
 1 [ 10       | 20 ]
]

RegexQuery -> Valuebase | StrBase | IntBase | FloatBase

db.regexQuery("^\d",[0]).print()

# Output

<ValueBase with 1 row>
Items = [
 * [ Name | ID ]
 0 [ 10   | 20 ]
]

Fetch -> List

search=db.fetch("2",False,[db.c("ID")]) # Same query parameters with above.
print(search) # Fetch method returns a list that contains lists.

# Output
[['Furkan', 21], [10, 20]]

Remove -> None

search=db.query("2",False,[0,1]) # This gives 2 entries.
db-=search[1] # Removes the 2nd one.
db.print()

# Output
<ValueBase with 3 rows>
Items = [
 * [    Name    |  ID ]
 0 [ "Elagoht"  | 17  ]
 1 [ "Furkan"   | 21  ]
 2 [ "Baytekin" | "1" ]
]

IndexOf -> int

print(db.indexof(
    db.query("2",False,db.range("Name","ID"))[0]
    # Given row must be exact, so you can use query and indices.
    # range method returned range(0,1) object which Name=0 ID=1.
))

# Output
1

Sort -> None

db.sort(0)
db.print("Hello this is a prefix string: ","Hello, this is a suffix string.")

# Output
Hello this is a prefix string: <ValueBase with 3 rows>
Items = [
 * [    Name    |  ID ]
 0 [ "Baytekin" | "1" ]
 1 [ "Elagoht"  | 17  ]
 2 [ "Furkan"   | 21  ]
] Hello, this is a suffix string.

Mirror -> None | Valuebase | StrBase | IntBase | FloatBase

db.mirror()
db.print()

#Output
<ValueBase with 3 rows>
Items = [
 * [  ID |    Name    ]
 0 [ 17  | "Elagoht"  ]
 1 [ 21  | "Furkan"   ]
 2 [ "1" | "Baytekin" ]
]

Flip -> None | Valuebase | StrBase | IntBase | FloatBase

db.flip()
db.print()

#Output
<ValueBase with 3 rows>
Items = [
 * [  ID |    Name    ]
 0 [ "1" | "Baytekin" ]
 1 [ 21  | "Furkan"   ]
 2 [ 17  | "Elagoht"  ]
]

Rotate -> None | Valuebase | StrBase | IntBase | FloatBase

db.rotate(-1)
db.renameColumns("Column","Names","Changed")
db.print()

#Output
<ValueBase with 2 rows>
Items = [
 * [   Column   |  Names   |  Changed  ]
 0 [ "Baytekin" | "Furkan" | "Elagoht" ]
 1 [ "1"        | 21       | 17        ]
]

Transpose -> None | Valuebase | StrBase | IntBase | FloatBase

db.transpose()
db.print()

#Output
<ValueBase with 3 rows>
Items = [
 * [     ID     | Name ]
 0 [ "Baytekin" | "1"  ]
 1 [ "Furkan"   | 21   ]
 2 [ "Elagoht"  | 17   ]
]

Example IntBase

db=sq.IntBase([1,2,pi],colNames=["First","Second","Third"]) # Instead of renameColumns, we used colNames parameter to name columns.
db+=(2,-(-pi),4)
db+=["pi",4,5]
db.addMany([4,5,6],(5,6,7),(6,7,8),{7:0,8:1,9:2})
db.print()

#Output
<IntBase with 7 rows>
Items = [
 * [ First | Second | Third ]
 0 [ 1     | 2      | 3     ]
 1 [ 2     | 3      | 4     ]
 2 [ 3     | 4      | 5     ]
 3 [ 4     | 5      | 6     ]
 4 [ 5     | 6      | 7     ]
 5 [ 6     | 7      | 8     ]
 6 [ 7     | 8      | 9     ]
]

Some of Math Methods

print("Quartiles\t:",       db.quartiles(0),"\n"
      "Mean\t\t:"   ,       db.mean(1)     ,"\n"
      "Median\t\t:" ,       db.median(1)   ,"\n"
      "Modes\t\t:"  ,       db.modes(1)    ,"\n"
      "Maximum\t\t:",       db.max(1)      ,"\n"
      "Minimum\t\t:",       db.min(1)      ,"\n"
      "18th Percentile\t:", db.percentile(18,1))

#Output
Quartiles       : (2.5, 5.5)
Mean            : 5.0
Median          : 5
Modes           : (2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0)
Maximum         : 8.0
Minimum         : 2.0
18th Percentile : 3.08

Summary -> String

print(db.summary(0))

#Output
*** Column 0 ***
Minimum         : 1.0
1st Quarter     : 2.5
Median          : 4
3rd Quarter     : 5.5
Maximum         : 7.0
Mean            : 4.0

Summaries -> String

print(db.summaries())

#Output
*** First ***
Minimum         : 1.0
1st Quarter     : 2.5
Median          : 4
2ns Quarter     : 5.5
Maximum         : 7.0
Mean            : 4.0
*** Second ***
Minimum         : 2.0
1st Quarter     : 3.5
Median          : 5
2ns Quarter     : 6.5
Maximum         : 8.0
Mean            : 5.0
*** Third ***
Minimum         : 3.0
1st Quarter     : 4.5
Median          : 6
2ns Quarter     : 7.5
Maximum         : 9.0
Mean            : 6.0

Change DataBase Type

db=db.changeType(sq.FloatBase)
db.increase(.2)
db.print()

#Output
<FloatBase with 7 rows>
Items = [
 * [ First | Second | Third ]
 0 [  1.20 |   2.20 |  3.20 ]
 1 [  2.20 |   3.20 |  4.20 ]
 2 [  3.20 |   4.20 |  5.20 ]
 3 [  4.20 |   5.20 |  6.20 ]
 4 [  5.20 |   6.20 |  7.20 ]
 5 [  6.20 |   7.20 |  8.20 ]
 6 [  7.20 |   8.20 |  9.20 ]
]

Split -> Tuple(Valuebase | StrBase | IntBase | FloatBase, Valuebase | StrBase | IntBase | FloatBase)

db0,db1=db.split(5)

db0.print(2,"","I am the first part.")
db1.print(2,"","I am the last part.")

#Output
<FloatBase with 5 rows>
Items = [
 * [ First | Second | Third ]
 0 [  1.20 |   2.20 |  3.20 ]
 1 [  2.20 |   3.20 |  4.20 ]
 2 [  3.20 |   4.20 |  5.20 ]
 3 [  4.20 |   5.20 |  6.20 ]
 4 [  5.20 |   6.20 |  7.20 ]
] I am the first part.
<FloatBase with 2 rows>
Items = [
 * [ First | Second | Third ]
 0 [  6.20 |   7.20 |  8.20 ]
 1 [  7.20 |   8.20 |  9.20 ]
] I am the last part.

Compare -> IntBase | FloatBase

db.compare(5,"<=",0).print()
# Get rows that are less than/equal to 5 in the first column.

#Output
<FloatBase with 4 rows>
Items = [
 * [ First | Second | Third ]
 0 [  1.20 |   2.20 |  3.20 ]
 1 [  2.20 |   3.20 |  4.20 ]
 2 [  3.20 |   4.20 |  5.20 ]
 3 [  4.20 |   5.20 |  6.20 ]
]

CompareMany -> IntBase | FloatBase

db.compareMany([7,1,4],("<=",">","!="),(0,0,0)).print()
# Get rows that are less than/equal to 7 in the first row, more than 1 in the second row, and not equal to 4 in the third row.

#Output
<FloatBase with 6 rows>
Items = [
 * [ First | Second | Third ]
 0 [  1.20 |   2.20 |  3.20 ]
 1 [  2.20 |   3.20 |  4.20 ]
 2 [  3.20 |   4.20 |  5.20 ]
 3 [  4.20 |   5.20 |  6.20 ]
 4 [  5.20 |   6.20 |  7.20 ]
 5 [  6.20 |   7.20 |  8.20 ]
]

Apply -> None

db.apply(lambda x,y: pow(x,2) if x%2 else -x,[])

# Empty list for applying all columns. Simply doing not fill this parameter does same. 

# It means if the value is odd, take square, else multiply with minus one.

# The "x" refers to input, the "y" refers to row number. You should pass the variable even if you not use it.

db.print()

#Output
<FloatBase with 7 rows>
Items = [
 * [ First | Second | Third ]
 0 [  1.44 |   4.84 | 10.24 ]
 1 [  4.84 |  10.24 | 17.64 ]
 2 [ 10.24 |  17.64 | 27.04 ]
 3 [ 17.64 |  27.04 | 38.44 ]
 4 [ 27.04 |  38.44 | 51.84 ]
 5 [ 38.44 |  51.84 | 67.24 ]
 6 [ 51.84 |  67.24 | 84.64 ]
]

Determine if a row in Database. -> Bool

print(
    [1.44, 4.840000000000001, 10.240000000000002] in db, # This is a float problem does in all programming languages.
    [1,+2,3] in db,
sep="\n")

#Output
True
False

Example IntBase for Missing Values

db=sq.IntBase((1,2,3),colNames=["These","Are","Columns"])
db+=3,
db+=4,5
db+=6,2,1

db.print()

#Output
<IntBase with 4 rows>
Items = [
 * [ These | Are  | Columns ]
 0 [ 1     | 2    | 3       ]
 1 [ 3     | ]
 2 [ 4     | 5    | ]
 3 [ 6     | 2    | 1       ]
]

Row 1 ends in 1st column and row 2 ends in 2nd column because there are null data.

FillNull -> None

db.fillNull()
db.print()

#Output
<IntBase with 4 rows>
Items = [
 * [ These | Are  | Columns ]
 0 [ 1     | 2    | 3       ]
 1 [ 3     | None | None    ]
 2 [ 4     | 5    | None    ]
 3 [ 6     | 2    | 1       ]
]

FillNone -> None

db.fillNone(-1)
db.print()

#Output
<IntBase with 4 rows>
Items = [
 * [ These | Are | Columns ]
 0 [ 1     | 2   | 3       ]
 1 [ 3     | -1  | -1      ]
 2 [ 4     | 5   | -1      ]
 3 [ 6     | 2   | 1       ]
]

Replaced all None values with -1.

CloneFromColumns -> Valuebase | StrBase | IntBase | FloatBase

db.cloneFromColumns(0,db.c("Columns")).print()

#Output
<IntBase with 4 rows>
Items = [
 * [ These | Columns ]
 0 [ 1     | 3       ]
 1 [ 3     | -1      ]
 2 [ 4     | -1      ]
 3 [ 6     | 1       ]
]

CloneFromColumnRange -> Valuebase | StrBase | IntBase | FloatBase

db.cloneFromColumnRange(1,db.c("Columns")).print()

#Output
<IntBase with 4 rows>
Items = [
 * [ Are | Columns ]
 0 [ 2   | 3       ]
 1 [ -1  | -1      ]
 2 [ 5   | -1      ]
 3 [ 2   | 1       ]
]

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