Basics

# Everything after the hash symbol is ignored by Python
 
# Display the documentation for the max function
help(max) 
 
# Get the type of an object — this returns str
type('a')    

# Import a package without an alias
import pandas                
 
# Import a package with an alias
import pandas as pd          
 
# Import an object from a package
from pandas import DataFrame 

import os                          # Import the operating system package
os.getcwd()                        # Get the current directory
os.setcwd("new/working/directory") # Set the working directory to a new file path

10 + 2    # Add two numbers with +
10 - 2    # Subtract a number with -
4 * 6     # Multiply two numbers with *
22 / 7    # Divide a number by another with /
22 // 7   # Integer divide a number with //
3 ** 4    # Raise to the power with **
22 % 7    # Get the remainder after division with %

a = 5         # Assign a value to a
x[0] = 1      # Change the value of an item in a list

3 == 3    # Test for equality with ==
3 != 3    # Test for inequality with !=
3 > 1     # Test greater than with >
3 >= 3    # Test greater than or equal to with >=
3 < 4     # Test less than with <
3 <= 4    # Test less than or equal to with <=

~(2 == 2)             # Logical NOT with ~
(1 != 1) & (1 < 1)    # Logical AND with &
(1 >= 1) | (1 < 1)    # Logical OR with |
(1 != 1) ^ (1 < 1)    # Logical XOR with ^

x = [1, 2, 3]    # Create lists with [], elements separated by commas

sorted(x)      # Return a sorted copy of the list e.g., [1,2,3]
x.sort()       # Sorts the list in place (replaces x)
reversed(x)    # Reverse the order of elements in x e.g., [2,3,1]
x.reverse()    # Reverse the list in place
x.count(2)     # Count the number of element 2 in the list

x = ['a', 'b', 'c', 'd', 'e']    # Define the list
x[0]        # Select the 0th element in the list
x[-1]       # Select the last element in the list
x[1:3]      # Select 1st (inclusive) to 3rd (exclusive)
x[2:]       # Select the 2nd to the end
x[:3]       # Select 0th to 3rd (exclusive)

x = [1, 3, 6]
y = [10, 15, 21]
x + y       # Returns [1, 3, 6, 10, 15, 21]
3 * x       # Returns [1, 3, 6, 1, 3, 6, 1, 3, 6]

# Create a dictionary with {}
{'a': 1, 'b': 4, 'c': 9}    

# Define the x dictionary
x = {'a': 1, 'b': 2, 'c': 3}   
 
# Get the keys of a dictionary, returns dict_keys(['a', 'b', 'c'])
x.keys()    
 
# Get the values of a dictionary, returns dict_values([1, 2, 3])
x.values()    
 
# Get a value from a dictionary by specifying the key
x['a']      

np.array([1, 2, 3])         # Convert a Python list to a NumPy array
np.arange(1, 5)             # Return a sequence from start (inclusive) to end (exclusive)
np.arange(1, 5, 2)          # Return a stepped sequence
np.repeat([1, 3, 6], 3)     # Repeat values n times
np.tile([1, 3, 6], 3)       # Repeat values n times

np.log(x)            # Calculate logarithm
np.exp(x)            # Calculate exponential
np.max(x)            # Get maximum value
np.min(x)            # Get minimum value
np.sum(x)            # Calculate sum
np.mean(x)           # Calculate mean
np.quantile(x, q)    # Calculate qth quantile
np.round(x, n)       # Round to n decimal places
np.var(x)            # Calculate variance
np.std(x)            # Calculate standard deviation

"Ali Azlan"    # Create a string with double or single quotes
"He said, \"Ali Azlan\""    # Embed a quote in string with the escape character \
 
"""
A Frame of Data
Tidy, Mine, Analyze It
Now You Have Meaning
"""    # Create multi-line strings with triple quotes

str[0]        # Get the character at a specific position
str[0:2]      # Get a substring from starting to ending index (exclusive)

"Data" + "Framed"     # Concatenate strings with +
3 * "data "           # Repeat strings with *
"beekeepers".split("e")    # Split a string on a delimiter

str = "Jack and Jill"
str.upper()           # Convert a string to uppercase
str.lower()           # Convert a string to lowercase
str.title()           # Convert a string to title case
str.replace("J", "P") # Replace matches of a substring

# Create a dataframe from a dictionary
pd.DataFrame({
    'a': [1, 2, 3],
    'b': np.array([4, 4, 6]),
    'c': ['x', 'x', 'y']
})
 
# Create a dataframe from a list of dictionaries
pd.DataFrame([
    {'a': 1, 'b': 4, 'c': 'x'},
    {'a': 1, 'b': 4, 'c': 'x'},
    {'a': 3, 'b': 6, 'c': 'y'}
])

df.iloc[3]              # Select the 3rd row
df['col']              # Select one column by name
df[['col1', 'col2']]   # Select multiple columns by names
df.iloc[:, 2]          # Select 2nd column
df.iloc[3, 2]          # Select the element in the 3rd row, 2nd column

pd.concat([df, df])                     # Concatenate DataFrames vertically
pd.concat([df,df], axis=1)              # Concatenate DataFrames horizontally
df.query('logical_condition')           # Get rows matching a condition
df.drop(columns=['col_name'])           # Drop columns by name
df.rename(columns={"oldname": "newname"})   # Rename columns
df.assign(temp_f=9/5 * df['temp_c'] + 32)  # Add a new column
df.mean()                               # Calculate the mean of each column
df.agg(aggregation_function)            # Get summary statistics by column
df.drop_duplicates()                    # Get unique rows
df.sort_values(by='col_name')           # Sort by values in a column
df.nlargest(n, 'col_name')             # Get rows with largest values in a column