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Basic Data Types
Constant values by the types
Value | Python Expression |
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Hexa decimal a1 | 0xa1 |
3.2e-12 | |
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String
String concatenation by join()
Write the code for a Python function expand(x) that takes a list of strings, concatenates them, and returns the resulting string repeated three times.
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def expand(x): return ''.join(x) * 3 |
Print string n times
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n = 2;
s ="Programming";
print(s * n); # ProgrammingProgramming |
Basic Operators
Python Arithmetic Operators
Operator | Description | Example |
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+ Addition | Adds values on either side of the operator. | 10 + 20 = 30 |
- Subtraction | Subtracts right hand operand from left hand operand. | 10 – 20 = -10 |
* Multiplication | Multiplies values on either side of the operator | 10 * 20 = 200 |
/ Division | Divides left hand operand by right hand operand | 20 / 10 = 2 |
% Modulus | Divides left hand operand by right hand operand and returns remainder | 20 % 10 = 0 |
** Exponent | Performs exponential (power) calculation on operators | 10**20 =10 to the power 20 |
// | Floor Division - The division of operands where the result is the quotient in which the digits after the decimal point are removed. But if one of the operands is negative, the result is floored, i.e., rounded away from zero (towards negative infinity) − | 9//2 = 4 and 9.0//2.0 = 4.0 -11//3 = -4 -11.0//3 = -4.0 |
Python Comparison Operators
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Below example is based on the condition as a=10, b=20 |
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Operator | Description | Example |
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== | If the values of two operands are equal, then the condition becomes true. | (a == b) is not true. |
!= | If values of two operands are not equal, then condition becomes true. | (a != b) is true. |
<> | If values of two operands are not equal, then condition becomes true. | (a <> b) is true. This is similar to != operator. |
> | If the value of left operand is greater than the value of right operand, then condition becomes true. | (a > b) is not true. |
< | If the value of left operand is less than the value of right operand, then condition becomes true. | (a < b) is true. |
>= | If the value of left operand is greater than or equal to the value of right operand, then condition becomes true. | (a >= b) is not true. |
<= | If the value of left operand is less than or equal to the value of right operand, then condition becomes true. | (a <= b) is true. |
Data Structures - List / Set / Tuple / Dictionary
List
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list1 = ['physics', 'chemistry', 1997, 2000]; list2 = [1, 2, 3, 4, 5 ]; list3 = ["a", "b", "c", "d"] |
Split string as list
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sentence = "the quick brown fox jumps over the lazy dog" words = sentence.split() print(words) |
Filter positive numbers only - 1
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numbers = [34.6, -203.4, 44.9, 68.3, -12.2, 44.6, 12.7] newlist = [] for number in numbers: if number>0: newlist.append(number) print(newlist) |
Filter positive numbers only - 2
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numbers = [34.6, -203.4, 44.9, 68.3, -12.2, 44.6, 12.7] newlist = [int(x) for x in numbers if x > 0] print(newlist) |
Create word list from a sentence with no duplicate entries
set() removes all the duplicate entries in the array
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strings = "my name is Chun Kang and Chun is my name" r = set(strings.split()) print(r) |
Python List REPL sessions
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a = ['foo', 'bar', 'baz', 'qux', 'quux', 'corge'] print(a[:] is a) print(max(a[2:4] + ['grault'])) print(a[-5:-3]) print(a[-6]) print(a[4::-2]) |
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False qux ['bar', 'baz'] foo ['quux', 'baz', 'foo'] |
Delete element in Python List
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a = [1, 2, 3, 4, 5] # option 1 - delete element 3 del a[2] # option 2 - delete element 3 a.remove(3) # option 3 - delete element 3 a[2:3] = [] |
Tuple
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The tuples cannot be changed unlike lists and tuples use parentheses, whereas lists use square brackets. |
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tup1 = ('physics', 'chemistry', 1997, 2000); tup2 = (1, 2, 3, 4, 5 ); tup3 = "a", "b", "c", "d"; |
Ascending sort
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# set pySet = {'e', 'a', 'u', 'o', 'i'} print(sorted(pySet)) # dictionary pyDict = {'e': 1, 'a': 2, 'u': 3, 'o': 4, 'i': 5} print(sorted(pyDict)) # tuple pyTuple = ('e', 'a', 'u', 'o', 'i') print(sorted(pyTuple)) |
Descending sort
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# set pySet = {'e', 'a', 'u', 'o', 'i'} print(sorted(pySet, reverse=True)) # dictionary pyDict = {'e': 1, 'a': 2, 'u': 3, 'o': 4, 'i': 5} print(sorted(pyDict, reverse=True)) # tuple pyTuple= ('e', 'a', 'u', 'o', 'i') print(sorted(pyTuple, reverse=True)) |
Set
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Unordered collections of unique elements |
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Set(['Jane', 'Marvin', 'Janice', 'John', 'Jack']) Set(['Janice', 'Jack', 'Sam']) Set(['Jane', 'Zack', 'Jack']) Set(['Jack', 'Sam', 'Jane', 'Marvin', 'Janice', 'John', 'Zack']) |
Implement the unique_names by combining two lists
The returned list should have no duplicates and it must be sorted.
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def unique_names(names1, names2): names1 += names2 return sorted( set(names1)) names1 = ["Ava", "Emma", "Olivia"] names2 = ["Olivia", "Sophia", "Emma"] print(unique_names(names1, names2)) # should print Ava, Emma, Olivia, Sophia |
Find overlapped entries from two arrays
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a = set([ "Seoul", "Pusan", "Incheon", "Mokpo" ]) b = set([ "Seoul", "Incheon", "Suwon", "Daejeon", "Gwangjoo", "Taeku"]) print(a.intersection(b)) print(b.intersection(a)) |
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Result |
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{'Seoul', 'Incheon'} {'Seoul', 'Incheon'} |
Find different elements from two arrays based on "symmetric_difference" method
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a = set(["Jake", "John", "Eric"]) b = set(["John", "Jill"]) print(a.symmetric_difference(b)) print(b.symmetric_difference(a)) |
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Result |
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{'Jake', 'Eric', 'Jill'} {'Eric', 'Jake', 'Jill'} |
Find different elements from two arrays based on "difference" method
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a = set(["Jake", "John", "Eric"]) b = set(["John", "Jill"]) print(a.difference(b)) print(b.difference(a)) |
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{'Jake', 'Eric'} {'Jill'} |
Find different elements from two arrays based on "union" method
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a = set(["Jake", "John", "Eric"]) b = set(["John", "Jill"]) print(a.union(b)) |
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{'John', 'Eric', 'Jake', 'Jill'} |
Print out a set containing all the participants from event A which did not attend event B
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a = ["Jake", "John", "Eric"] b = ["John", "Jill"] print(set(a).difference(set(b))) |
Find sorted unique names in two list
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def unique_names(names1, names2): return sorted(set(names1+names2)) names1 = ["Ava", "Emma", "Olivia"] names2 = ["Olivia", "Sophia", "Emma"] print(unique_names(names1, names2)) # should print Ava, Emma, Olivia, Sophia |
Dictionary
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Python dictionaries are similar to lists in that they are mutable and can be nested to any arbitrary depth (constrained only by available memory). A dictionary can contain any type of Python object, including another dictionary. The keys in a given dictionary do not need to be the same type as one another, nor do the values. Dictionary elements are accessed by key. Unlike with list indexing, the order of the items in a dictionary plays no role in how the items are accessed. Even though dictionary access does not rely on item order, as of version 3.7 the Python language specification does guarantee that the order of items in a dictionary is maintained once the dictionary is created. |
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dict = {'Name': 'Zara', 'Age': 7, 'Class': 'First'} |
Get last name from full name by split()
The function can be easily implemented by string method
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actor = {"name": "John Cleese", "rank": "awesome"} def get_last_name(): return actor["name"].split()[1] get_last_name() print("All exceptions caught! Good job!") print("The actor's last name is %s" % get_last_name()) |
Accessing dictionary values
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x = [ 'a', 'b', { 'foo': 1, 'bar': { 'x' : 10, 'y' : 20, 'z' : 30 }, 'baz': 3 }, 'c', 'd' ] print(x[2]['bar']['z']) |
Result
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30 |
Delete a dictionary element
Deleting a dictionary element by statement
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Deleting a dictionary element by method
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d.pop('foo') |
Copying a dictionary
Method 1)
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d2 = dict(d1) |
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d2 = {} d2.update(d1) |
Hashmap
Python kindly provides a built-in type hashmap named as dictionary. You can easily hash like below:
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myHashmap = {} # hash by name myHashmap['Chun'] = 1234 myHashmap['John'] = 2342 # print hash value print( myHashmap['Chun']) |
Generator
Random number generation
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import random def lottery(): # returns 6 numbers between 1 and 40 for i in range(6): yield random.randint(1, 40) # returns a 7th number between 1 and 15 yield random.randint(1,15) for random_number in lottery(): print("And the next number is... %d!" %(random_number)) |
Swap variables' value
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a = 1 b = 2 a, b = b, a print(a,b) |
Fibonacci series generator
The first two numbers of the series is always equal to 1, and each consecutive number returned is the sum of the last two numbers - the below code uses only two variables to get the result.
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def fib(): a, b = 1, 1 while 1: yield a a, b = b, a + b # testing code import types if type(fib()) == types.GeneratorType: print("Good, The fib function is a generator.") counter = 0 for n in fib(): print(n) counter += 1 if counter == 10: break |
Function Arguments(Parameters)
Multiple Function Argument recognition - the list of "therest" parameters
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def foo(first, second, third, *therest): print("First: %s" %(first)) print("Second: %s" %(second)) print("Third: %s" %(third)) print("And all the rest... %s" %(list(therest))) foo(1,2,3,4,5) |
Multiple Function Argument by keyword
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def bar(first, second, third, **options): if options.get("action") == "sum": print("The sum is: %d" %(first + second + third)) if options.get("number") == "first": return first result = bar(1, 2, 3, action = "sum", number = "first") print("Result: %d" %(result)) |
Regular Expression
RegEx(Regular Expressions) to search "[on]" or "[off]" on the string
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import re pattern = re.compile(r"\[(on|off)\]") # Slight optimization print(re.search(pattern, "Mono: Playback 65 [75%] [-16.50dB] [on]")) |
RegEx(Regular Expression) to check email address
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import re def test_email(your_pattern): pattern = re.compile(your_pattern) emails = ["john@example.com", "python-list@python.org", "wha.t.`1an?ug{}ly@email.com"] for email in emails: if not re.match(pattern, email): print("You failed to match %s" % (email)) elif not your_pattern: print("Forgot to enter a pattern!") else: print("Pass") pattern = r"[a-z0-9]+@[a-z0-9]+\.[a-z0-9]+" test_email(pattern) |
Exception Handling
try/except block
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def do_stuff_with_number(n): print(n) def catch_this(): the_list = (1, 2, 3, 4, 5) for i in range(20): try: do_stuff_with_number(the_list[i]) except IndexError: # Raised when accessing a non-existing index of a list do_stuff_with_number('out of bound - %d' % i) catch_this() |
Numpy
Convert arrays to Numpy arrays
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# Create 2 new lists height and weight height = [1.87, 1.87, 1.82, 1.91, 1.90, 1.85] weight = [81.65, 97.52, 95.25, 92.98, 86.18, 88.45] # Import the numpy package as np import numpy as np # Create 2 numpy arrays from height and weight np_height = np.array(height) np_weight = np.array(weight) print(type(np_height)) # Calculate bmi bmi = np_weight / np_height ** 2 # Print the result print(bmi) # For a boolean response print(bmi > 23) # Print only those observations above 23 print(bmi[bmi > 23]) |
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<class 'numpy.ndarray'> [ 23.34925219 27.88755755 28.75558507 25.48723993 23.87257618 25.84368152] [ True True True True True True] [ 23.34925219 27.88755755 28.75558507 25.48723993 23.87257618 25.84368152] |
Convert all of the weights from kilograms to pounds based in NumPy
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weight_kg = [81.65, 97.52, 95.25, 92.98, 86.18, 88.45] import numpy as np # Create a numpy array np_weight_kg from weight_kg np_weight_kg = np.array(weight_kg) # Create np_weight_lbs from np_weight_kg np_weight_lbs = np_weight_kg * 2.2 # Print out np_weight_lbs print(np_weight_lbs) |
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[ 179.63 214.544 209.55 204.556 189.596 194.59 ] |
Pandas DataFrame / CSV / Join / Merge
Create a Pandas DataFrame based on array
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dict = {"country": ["Brazil", "Russia", "India", "China", "South Africa"], "capital": ["Brasilia", "Moscow", "New Dehli", "Beijing", "Pretoria"], "area": [8.516, 17.10, 3.286, 9.597, 1.221], "population": [200.4, 143.5, 1252, 1357, 52.98] } import pandas as pd brics = pd.DataFrame(dict) print(brics) |
Adding index to a Pandas DataFrame
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# Set the index for brics brics.index = ["BR", "RU", "IN", "CH", "SA"] # Print out brics with new index values print(brics) |
Reading CSV by Pandas DataFrame
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# Import pandas as pd import pandas as pd # Import the cars.csv data: cars cars = pd.read_csv('cars.csv') # Print out cars print(cars) |
CSV
Reading a CSV file by Pandas DataFrame with 1st column as index
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# Import pandas and cars.csv import pandas as pd cars = pd.read_csv('cars.csv', index_col = 0) # Print out country column as Pandas Series print(cars['cars_per_cap']) # Print out country column as Pandas DataFrame print(cars[['cars_per_cap']]) # Print out DataFrame with country and drives_right columns print(cars[['cars_per_cap', 'country']]) |
Save a Pandas DaraFrame by CSV format
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dict = {"country": ["Brazil", "Russia", "India", "China", "South Africa"], "capital": ["Brasilia", "Moscow", "New Dehli", "Beijing", "Pretoria"], "area": [8.516, 17.10, 3.286, 9.597, 1.221], "population": [200.4, 143.5, 1252, 1357, 52.98] } import pandas as pd brics = pd.DataFrame(dict) brics.to_csv('example.csv') |
Save a Pandas DaraFrame by CSV format with header and no index
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from pandas import DataFrame Cars = {'Brand': ['Honda Civic','Toyota Corolla','Ford Focus','Audi A4'], 'Price': [22000,25000,27000,35000] } df = DataFrame(Cars, columns= ['Brand', 'Price']) export_csv = df.to_csv (r'C:\Users\Ron\Desktop\export_dataframe.csv', index = None, header=True) #Don't forget to add '.csv' at the end of the path print (df) |
Print partial rows (observations) from a Pandas DataFrame
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# Import cars data import pandas as pd cars = pd.read_csv('cars.csv', index_col = 0) # Print out first 4 observations print(cars[0:4]) # Print out fifth, sixth, and seventh observation print(cars[4:6]) |
Data access by loc and iloc in Pandas DaraFrame - Select colums by index or name
loc is label-based, and iloc is integer index based
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# Import cars data import pandas as pd cars = pd.read_csv('cars.csv', index_col = 0) # Print out observation for Japan print(cars.iloc[2]) # Print out observations for Australia and Egypt print(cars.loc[['AUS', 'EG']]) |
Sort
Sort a Pandas DataFrame in an ascending order
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df.sort_values(by=['Brand'], inplace=True) |
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# sort - ascending order from pandas import DataFrame Cars = {'Brand': ['Honda Civic','Toyota Corolla','Ford Focus','Audi A4'], 'Price': [22000,25000,27000,35000], 'Year': [2015,2013,2018,2018] } df = DataFrame(Cars, columns= ['Brand', 'Price','Year']) # sort Brand - ascending order df.sort_values(by=['Brand'], inplace=True) print (df) |
Sort a Pandas DataFrame in a descending order
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df.sort_values(by=['Brand'], inplace=True, ascending=False) |
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# sort - descending order from pandas import DataFrame Cars = {'Brand': ['Honda Civic','Toyota Corolla','Ford Focus','Audi A4'], 'Price': [22000,25000,27000,35000], 'Year': [2015,2013,2018,2018] } df = DataFrame(Cars, columns= ['Brand', 'Price','Year']) # sort Brand - descending order df.sort_values(by=['Brand'], inplace=True, ascending=False) print (df) |
Sort a Pandas DataFrame by multiple columns
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df.sort_values(by=['First Column','Second Column',...], inplace=True) |
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# sort by multiple columns from pandas import DataFrame Cars = {'Brand': ['Honda Civic','Toyota Corolla','Ford Focus','Audi A4'], 'Price': [22000,25000,27000,35000], 'Year': [2015,2013,2018,2018] } df = DataFrame(Cars, columns= ['Brand', 'Price','Year']) # sort by multiple columns: Year and Price df.sort_values(by=['Year','Price'], inplace=True) print (df) |
Join and merge Pandas DataFrames
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import pandas as pd from IPython.display import display from IPython.display import Image raw_data = { 'subject_id': ['1', '2', '3', '4', '5'], 'first_name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'], 'last_name': ['Anderson', 'Ackerman', 'Ali', 'Aoni', 'Atiches']} df_a = pd.DataFrame(raw_data, columns = ['subject_id', 'first_name', 'last_name']) raw_data = { 'subject_id': ['4', '5', '6', '7', '8'], 'first_name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'], 'last_name': ['Bonder', 'Black', 'Balwner', 'Brice', 'Btisan']} df_b = pd.DataFrame(raw_data, columns = ['subject_id', 'first_name', 'last_name']) raw_data = { 'subject_id': ['1', '2', '3', '4', '5', '7', '8', '9', '10', '11'], 'test_id': [51, 15, 15, 61, 16, 14, 15, 1, 61, 16]} df_n = pd.DataFrame(raw_data, columns = ['subject_id','test_id']) # Join the two dataframes along rows df_new = pd.concat([df_a, df_b]) # Join the two dataframes along columns pd.concat([df_a, df_b], axis=1) # Merge two dataframes along the subject_id value pd.merge(df_new, df_n, on='subject_id') # Merge two dataframes with both the left and right dataframes using the subject_id key pd.merge(df_new, df_n, left_on='subject_id', right_on='subject_id') # Merge with outer join pd.merge(df_a, df_b, on='subject_id', how='outer') # Merge with inner join pd.merge(df_a, df_b, on='subject_id', how='inner') # Merge with right join pd.merge(df_a, df_b, on='subject_id', how='right') # Merge with left join pd.merge(df_a, df_b, on='subject_id', how='left') # Merge while adding a suffix to duplicate column names pd.merge(df_a, df_b, on='subject_id', how='left', suffixes=('_left', '_right')) # Merge based on indexes pd.merge(df_a, df_b, right_index=True, left_index=True) |
Get the maximum value of column in Pandas DataFrame
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import pandas as pd # Create a DataFrame d = { 'Name':['Alisa','Bobby','jodha','jack','raghu','Cathrine', 'Alisa','Bobby','kumar','Alisa','Alex','Cathrine'], 'Age':[26,24,23,22,23,24,26,24,22,23,24,24], 'Score':[85,63,55,74,31,77,85,63,42,62,89,77] } df = pd.DataFrame(d,columns=['Name','Age','Score']) # get the maximum values of all the column in dataframe - it will be raghu, 26, 89, object df.max() # get the maximum value of the column 'Age' - it will be 26 df['Age'].max() # get the maximum value of the column 'Name' - it will be raghu df['Name'].max() |
Get the minimum value of column in Pandas DataFrame
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import pandas as pd # Create a DataFrame d = { 'Name':['Alisa','Bobby','jodha','jack','raghu','Cathrine', 'Alisa','Bobby','kumar','Alisa','Alex','Cathrine'], 'Age':[26,24,23,22,23,24,26,24,22,23,24,24], 'Score':[85,63,55,74,31,77,85,63,42,62,89,77] } df = pd.DataFrame(d,columns=['Name','Age','Score']) # get the minimum values of all the column in dataframe - it will display Alex, 22, 31, object df.min() # get the minimum value of the column 'Age' - it will be 22 df['Age'].min() # get the minimum value of the column 'Name' - it will be Alex df['Name'].min() |
Select row with maximum and minimum value in Pandas DataFrame
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import pandas as pd # Create a DataFrame d = { 'Name':['Alisa','Bobby','jodha','jack','raghu','Cathrine', 'Alisa','Bobby','kumar','Alisa','Alex','Cathrine'], 'Age':[26,24,23,22,23,24,26,24,22,23,24,24], 'Score':[85,63,55,74,31,77,85,63,42,62,89,77]} df = pd.DataFrame(d,columns=['Name','Age','Score']) # get the row of max value df.loc[df['Score'].idxmax()] # get the row of minimum value df.loc[df['Score'].idxmin()] |
Get the unique values (rows) of a Pandas Dataframe
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import pandas as pd # Create a DataFrame d = { 'Name':['Alisa','Bobby','jodha','jack','raghu','Cathrine', 'Alisa','Bobby','kumar','Alisa','Alex','Cathrine'], 'Age':[26,24,23,22,23,24,26,24,22,23,24,24] } df = pd.DataFrame(d,columns=['Name','Age']) # get the unique values (rows) print df.drop_duplicates() # get the unique values (rows) by retaining last row print df.drop_duplicates(keep='last') |
Get the list of column headers or column name in a Pandas DataFrame
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import pandas as pd # Create a DataFrame d = { 'Name':['Alisa','Bobby','jodha','jack','raghu','Cathrine', 'Alisa','Bobby','kumar','Alisa','Alex','Cathrine'], 'Age':[26,24,23,22,23,24,26,24,22,23,24,24], 'Score':[85,63,55,74,31,77,85,63,42,62,89,77]} df = pd.DataFrame(d,columns=['Name','Age','Score']) # method 1: get list of column name list(df.columns.values) # method 2: get list of column name list(df) |
Delete or Drop the duplicate row of a Pandas DataFrame
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import pandas as pd # Create a DataFrame d = { 'Name':['Alisa','Bobby','jodha','jack','raghu','Cathrine', 'Alisa','Bobby','kumar','Alisa','Alex','Cathrine'], 'Age':[26,24,23,22,23,24,26,24,22,23,24,24], 'Score':[85,63,55,74,31,77,85,63,42,62,89,77]} df = pd.DataFrame(d,columns=['Name','Age','Score']) # drop duplicate rows df.drop_duplicates() # drop duplicate rows by retaining last occurrence df.drop_duplicates(keep='last') # drop duplicate by a column name df.drop_duplicates(['Name'], keep='last') |
Drop or delete the row in Pandas DataFrame with conditions
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import pandas as pd # Create a DataFrame d = { 'Name':['Alisa','Bobby','jodha','jack','raghu','Cathrine', 'Alisa','Bobby','kumar','Alisa','Alex','Cathrine'], 'Age':[26,24,23,22,23,24,26,24,22,23,24,24], 'Score':[85,63,55,74,31,77,85,63,42,62,89,77]} df = pd.DataFrame(d,columns=['Name','Age','Score']) # Drop an observation or row df.drop([1,2]) # Drop a row by condition df[df.Name != 'Alisa'] # Drop a row by index df.drop(df.index[2]) # Drop bottom 3 rows df[:-3] |
Reshape wide to long in Pandas DataFrame with melt() function
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import pandas as pd # Create a DataFrame d = { 'countries':['A','B','C'], 'population_in_million':[100,200,120], 'gdp_percapita':[2000,7000,15000] } df = pd.DataFrame(d,columns=['countries','population_in_million','gdp_percapita']) # shape from wide to long with melt function in pandas df2=pd.melt(df,id_vars=['countries'],var_name='metrics', value_name='values') |
Reshape long to wide in Pandas DataFrame with pivot function
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import pandas as pd # Create a DataFrame d = { 'countries':['A','B','C','A','B','C'], 'metrics':['population_in_million','population_in_million','population_in_million', 'gdp_percapita','gdp_percapita','gdp_percapita'], 'values':[100,200,120,2000,7000,15000] } df = pd.DataFrame(d,columns=['countries','metrics','values']) # reshape from long to wide in pandas python df2=df.pivot(index='countries', columns='metrics', values='values') |
Reshape using Stack() and unstack() function in Pandas DataFrame
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import pandas as pd header = pd.MultiIndex.from_product([['Semester1','Semester2'],['Maths','Science']]) d=([[12,45,67,56],[78,89,45,67],[45,67,89,90],[67,44,56,55]]) df = pd.DataFrame(d, index=['Alisa','Bobby','Cathrine','Jack'], columns=header) # stack the dataframe stacked_df=df.stack() # unstack the dataframe unstacked_df = stacked_df.unstack() # stack the dataframe of column at level 0 stacked_df_lvl=df.stack(level=0) # unstack the dataframe unstacked_df1 = stacked_df_lvl.unstack() |
MatPlotLib
Line chart by city and population by MatPlotLib
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from matplotlib import pyplot as plt plt.plot(["Seoul","Paris","Seattle"], [30,25,55]) plt.xlabel('City') plt.ylabel('Response') plt.title('Experiment Result') plt.show() |
Result
Bar chart by city and population by MatPlotLib
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from matplotlib import pyplot as plt plt.bar(["Seoul","Paris","Seattle"], [30,25,55]) plt.xlabel('City') plt.ylabel('Response') plt.title('Experiment Result') plt.show() |
Result
Spot graph by age and score by MapPlotLib
Below code shows a graph displaying multiple spots by age and score stored in a pandas data frame.
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import pandas as pd from matplotlib import pyplot as plt # Create a DataFrame d = { 'Name':['Alisa','Bobby','jodha','jack','raghu','Cathrine', 'Alisa','Bobby','kumar','Alisa','Alex','Cathrine'], 'Age':[26,24,23,22,23,24,26,24,22,23,24,24], 'Score':[85,63,55,74,31,77,85,63,42,62,89,77] } df = pd.DataFrame(d,columns=['Name','Age','Score']) plt.plot(df['Age'], df['Score'], 'ro') plt.xlabel('Age') plt.ylabel('Score') plt.title('Experiment Result') plt.show() |
Result
OS
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Draw Samsung's stock price by MatPlotLib
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import os
if not os.path.exists('test.png'):
# do something if 'test.png' does not exist
pass
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Algorithms
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matplotlib
matplotlib.use('Agg')
import pandas as pd
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
import os
samsung = pd.read_csv('http://qsok.com/test/py/005930.csv')
# samsung.set_index( 'Date', inplace=True)
print("<pre>")
print(samsung)
print("\n")
max_date = samsung['Date'].max()
print('Last date of record stored %s' % max_date)
print("</pre>")
if not os.path.exists('./samsung_stock_price.png'):
plt.plot(samsung['Date'], samsung['Close'])
plt.xlabel('Date')
plt.ylabel('Close')
plt.title('Experiment Result')
plt.savefig('samsung_stock_price.png')
print("<img src=samsung_stock_price.png>")
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Result
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OS
Check if file exists or not
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import os
if not os.path.exists('test.png'):
# do something if 'test.png' does not exist
pass
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Algorithms
Check all unique or not
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def all_unique(lst):
return len(lst) == len(set(lst))
x = [1,1,2,2,3,2,3,4,5,6]
y = [1,2,3,4,5]
all_unique(x) # False
all_unique(y) # True |
Palindrome number - Determine whether an integer is a palindrome
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def is_palindrome(word): j = len(word)-1 i =0 while i<j and word[i].lower()==word[j].lower(): i+=1 j-=1 return (i>=j) print(is_palindrome('Deleveled')) |
Two sum - return indices of the two numbers such that they add up to a specific target
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Given nums = [2, 7, 11, 15], target = 9, Because nums[0] + nums[1] = 2 + 7 = 9, |
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def twoSum(self, nums, target): seen = {} for i, v in enumerate(nums): remaining = target - v if remaining in seen: return [seen[remaining], i] seen[v] = i return [] |
Reverse integer - Given a 32-bit signed integer, reverse digits of an integer.
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Input: 123 → Output: 321 |
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class Solution(object): def reverse(self, x): if x >= 2**31-1 or x <= -2**31: return 0 else: strg = str(x) if x >= 0 : revst = strg[::-1] else: temp = strg[1:] temp2 = temp[::-1] revst = "-" + temp2 if int(revst) >= 2**31-1 or int(revst) <= -2**31: return 0 else: return int(revst) |
Merge two sorted linked lists and return it as a new list.
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Input: 1->2->4, 1->3->4 |
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class Solution: def mergeTwoLists(self, l1, l2): result = ListNode(0) # The new list we are going to eventually return head = result # keep a pointer to the head so we can return head.next in the end while(l1 != None and l2 != None): # This check is important in the case where one list is shorter than the other if l1.val < l2.val: # Add l1's value as a new node to result if its less than l2's result.next = ListNode(l1.val) l1 = l1.next result = result.next elif l2.val < l1.val: # Add l2's value as a new node to result if its less than l1's result.next = ListNode(l2.val) l2 = l2.next result = result.next else: # In this case, the values must be equal so add both to result and move the linked lists forward result.next = ListNode(l1.val) result = result.next result.next = ListNode(l2.val) result = result.next l1 = l1.next l2 = l2.next if l1 == None and l2 != None: # If l2 is longer than l1, add all of the remaining values of l2 to result while(l2 != None): result.next = ListNode(l2.val) result = result.next l2 = l2.next elif l2 == None and l1 != None: # if l1 is longer than l2, add all of the remaining values of l1 to result while(l1 != None): result.next = ListNode(l1.val) result = result.next l1 = l1.next return head.next # return the result |
Remove Duplicates from Sorted Array
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class Solution(object): def removeDuplicates(self, nums): if not nums: return 0 i = 1 bound = len(nums) prev = nums[0] while i < bound: if prev == nums[i]: nums.pop(i) bound = len(nums) else: prev = nums[i] i += 1 return len(nums) |
Longest common prefix
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Input: ["flower", "flow", "flight" ] → Output: "fl" Input: ["dog", "racecar", "car"] → Output: "" |
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class Solution: def longestCommonPrefix(self, strs: List[str]) -> str: if not strs: return "" result="" cnt=len(strs) for i in range(len(strs[0])): j=1 while j<cnt and i<len(strs[j]) and strs[j][i]==strs[0][i]: j+=1 if j==cnt: result += strs[0][i] else: break; return result; |
Longest Palindromic Substring
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Input: "babad" → Output: "bab" Input: "cbd" → Output: "bb" |
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class Solution: def longestPalindrome(self, s): """ :type s: str :rtype: str """ # Return if string is empty if not s: return s res = "" for i in range(len(s)): j = i + 1 # While j is less than length of string # AND res is *not* longer than substring s[i:] while j <= len(s) and len(res) <= len(s[i:]): # If substring s[i:j] is a palindrome # AND substring is longer than res if s[i:j] == s[i:j][::-1] and len(s[i:j]) > len(res): res = s[i:j] j += 1 return res |
Same Tree
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# Definition for a binary tree node. # class TreeNode: # def __init__(self, x): # self.val = x # self.left = None # self.right = None class Solution: def isSameTree(self, p: TreeNode, q: TreeNode) -> bool: if p==None and q!=None: return False if p!=None and q==None: return False if p==None and q==None: return True if p.val==q.val: return self.isSameTree(p.left, q.left) and self.isSameTree(p.right, q.right) |
Merge Sorted Array
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Given two sorted integer arrays nums1 and nums2, merge nums2 into nums1 as one sorted array. nums1 = [1,2,3,0,0,0], m = 3 Output: [1,2,2,3,5,6] |
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class Solution: def merge(self, nums1: List[int], m: int, nums2: List[int], n: int) -> None: """ Do not return anything, modify nums1 in-place instead. """ p = m+n-1 m -= 1 n -= 1 while p>=0: if n<0: break; elif m<0: nums1[p] = nums2[n] n-=1 elif nums1[m]<nums2[n]: nums1[p] = nums2[n] n-=1 else: nums1[p] = nums1[m] m-=1 p-=1 |
Minimum Depth of Binary Tree
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Given binary tree [3,9,20,null,null,15,7], 3 |
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# Definition for a binary tree node. # class TreeNode: # def __init__(self, x): # self.val = x # self.left = None # self.right = None class Solution: def minDepth(self, root: TreeNode) -> int: if root==None: return 0 if root.left==None and root.right==None: return 1 elif root.left!=None and root.right!=None: return min( self.minDepth(root.left), self.minDepth(root.right)) + 1 elif root.left==None: return self.minDepth(root.right) + 1 else: return self.minDepth(root.left) + 1 |
Reorder List
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Given a singly linked list L: L0→L1→…→Ln-1→Ln, ex1) Given 1->2->3->4, reorder it to 1->4->2->3. |
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# Definition for singly-linked list. # class ListNode: # def __init__(self, x): # self.val = x # self.next = None class Solution: def reorderList(self, head: ListNode) -> None: """ Do not return anything, modify head in-place instead. """ # collect value serially v=[] p=head while p!=None: v.append(p.val) p=p.next # reorder based on the given logic i=0 lcnt=len(v) j=lcnt-1 p=head while p!=None: p.val = v[i] i += 1 p=p.next if p!=None: p.val = v[j] j-=1 p = p.next |
Compact - Remove Falsy values (None, False, "")
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def compact(lst): return list(filter(None, lst)) compact([0, 1, False, 2, '', 3, 'a', 's', 34]) # [ 1, 2, 3, 'a', 's', 34 ] |
Python Simple Web Server
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import SimpleHTTPServer import SocketServer PORT = 8888 Handler = SimpleHTTPServer.SimpleHTTPRequestHandler httpd = SocketServer.TCPServer(("", PORT), Handler) print "serving at port", PORT httpd.serve_forever() |
You can run above as
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python -m simple_http_server.py j-=1 p = p.next |