Python 3

Python 3

1. Print

   • print("string")
   • print(variable)
     • print first letter = print(ListVariable[0])
       • print last letter = print(ListVariable[-1])
   • print(variable + "string") - called concatenate
     • print("Captain {} is {} years old.".format(name, age))
   • print("""Multi-line text""")

2. Syntax

   • "string"
     • """Multi-line text"""
     • combined_string = ("Captain {} is {} years old.".format(name, age))
       • combined_string_with_var_to_two_decimal_places = ("This items costs: {:.2f}".format(float)
     • Example_string = 'this_is_a_string'
       • newList = Example_string.split('_')
         • newList = ['this', 'is', 'a', 'string']
           • newString = '-'.join(newList)
             • newString = 'this-is-a-string'
         • Note: to seperate by tab key '\t' or new line '\n'
       • Example_string.strip('_')
         • removes '_'
       • Example_string.replace('_', ' ')
         • replaces first arg with second
       • Example_string.find('i')
         • returns list with indices of arg
   • int(1)
   • float(0.2)
     • round(float, No. of decimals)

3. def function(arg1, arg2):

   • return responseA, responseB
   • to set a default value: arg2=global_variable_name

4. Control Flow

   • If (not) condition 1 and/or (not) condition 2:
     • elif (not) condition 3
       • else: (not) condition 4
   • try: function()
     • except Type-of-error:
       • print "You raised an error" (for example)
   • for temp_variable in list:
     • if temp_variable == a condition that means it's not wanted:
       • continue
     • if temp_variable == a condition which can stop the search
       • break

5. Lists

   • list = [data]
     • list = [[sub-list], [sub-list2]]
   • listA_and_B = zip(listA, listB)
     • - Note this will create a zip object not a list
       • listA_and_B = list(ListA_and_B)
   • list.append(data)
     • list2 = list1 + [data1, data2]
     • list = [data1, data2, data3]
       • newList = [var for var in list if var (condition)]
   • len(list)
   • list[0] (first) list[-1] (last)
     • list[0:2] (first 3)
       • list[2:4] (third and fourth)
         • list[-2:] (last two items)
   • list.count("Character") - returns int
   • list.sort() - sorts original list, doesn't return anything
   • newList = sorted(list) - returns a new list
   • list = list[1:] - removes first datum
     • list = list[:-1] - removes last datum

6. Tuples

   • my_tuple = (Data1, Data2, Data3)
     • Works like a list to store multiple data types
       • can print elements with my_tuple[x]
         • Can NOT overwrite data (inmutable)
     • Var1, Var2, Var3 = my_tuple

7. PANDAS / CSV

   • import pandas as pd
     • import csv
       • Data is put in a dataframe
         • var = pd.read_csv('NameOfFile.csv')
           • var = pd.read_xlsx('NameOfFile.xlsx')
             • excel file
             • var = pd.read_csv('NameOfFile.txt')
               • USING THE DATA
                 • print(dfvar.head(number_of_rows))
                   • prints top rows
                 • print(dfvar.tail(number_of_rows))
                   • prints bottom rows
                 • print(dfvar.columns)
                   • prints a list type format
                   • print(dfvar.columns[i])
                     • prints column at index i
                   • number of columns = len(dfvar.columns)
                 • print all in column - print(dfvar[Name_of_column])
                   • multiple columns, use a list - print(dfvar[[column1, column2]]
                 • iloc = index location function
                   • print rows X to Y - print(dfvar.iloc[rowX: rowY)
                   • print specific data point X,Y - print(dfvar.iloc[X, Y])
                 • iterrows = iterate through rows
                   • i.e. for every index, return data in all rows
                   • for index, row in dfvar.iterrows():
                     • print(index, row)
                       • to only print data from certain columns;-
                         • print(index, row[column1: etc])
                 • iterating through index for data with conditions
                   • print rows that meed condition using .loc ;-
                     • print(dfvar.loc[dfvar['Column Name'] == 'Condition']
                 • dfvar.describe
                   • gives you information such as standard deviation & mean etc.
                 • Sorting
                   • dfvar.sort_values('Column_name')
                     • sorts alphabetically
                   • dfvar.sort_values(['Column 1', 'Column 2'], ascending = [1,0])
                     • ascending = 1 means true, i.e alphebetical or 0 - 100
                       • 0 = false i.e. reverse
                 • • EDITING THE DATA
                     • Adding Columns
                       • dfvar['New_Column_Name'] = dfvar['Column1'] + dfvar['Column2']
                         • or
                           • dfvar['New_Column_Name'] = df.iloc[XColumn: YColumn, XRow:YRow].sum(axis = 1)
                             • note df.iloc[] locates the data within those ranges
                               • .sum(axis = 1) specifies adding them all together horizontally
                                 • axis = 0 would be vertically
                             • To move the position of the new column(s);-
                               • newList = list(dfvar.columns.values)
                                 • dfvar = dfvar[newList[X:Y] + [newList[-1]] + newList[Y+1:Z]]
                                   • The range X:Y is the columns you want before
                                     • newList[-1] is the final column that you have added
                                       • newList[Y+1:Z] is the remaining columns to go on the end
                                       • Note: newList[-1] is one column and will be considered a string, hence the extra square brackets
                     • Dropping a column
                       • dfvar = dfvar.drop(columns=['Column_name_to_be_removed']
                     • EXPORTING THE DATA
                       • dfvar.to_csv('filename.csv', index=False)
                         • Index=False prevents adding the index numbers as the left most column
                         • dfvar.to_excel('filename.xlsx', index=False)
                           • dfvar.to_csv('filename.txt', index=False, sep = '\t')
                             • sep='\t' means they will be seperated by a tab key
               • text file - note data is imported to single column
                 • to import to tab seperated;-
                   • var = pd.read_csv('NameOfFile.txt', delimiter=\t)
                     • where the '\t' denotes the tab key
           • comma seperated values file
         • length of data = len(var)

8. Dictionairys

   • my_dict = {}
     • my_dict["new key"] = value
     • my_dict.update({key1: value1, key2: value2])
   • Combing two lists into a dictionairy
     • new_dict = {key:value for key, value in zip(list1, list2)}

9. CLASSES

   • class ClassName: