Python syntax

• type(x) used to determine the data type
• """ """ multiple line comment
• arr = [1,2,3] list
• arr = [1,2,3,4,5,]

  print(arr[2:4])

  [3,4]
• len(arr) length
• arr.append(1) add item to the end of the list
• arr.remove(1) remove the value in a list
• arr.pop() remove the last element in a list
• del arr[0] delete using index in a list
• arr.clear() clears the list
• list3 = list1 + list2 join two lists (1st method)
• for i in list2:

  list1.append(i)

  
  

  joint two lists(2nd method)
• list1.extend(list2) join two lists(3rd method)
• arr = (1, 2, 3, 4, 5) tuple
• arr = {1, 2, 3,} set
• arr = {'a': 1, 'b': 2, 'c': 3} dictionary
• tuple = (1,4,3)

  list = list(tuple)

  list[1] = 2

  tuple = tuple(list)

  
  

  changing tuple values
• set3 = set1.union(set2) join two sets (1st method)
• set1.update(set2) join two sets(2nd method)
• pip install numpy to install numpy
• import numpy as np to import numpy library
• import array as arr to import array library
• create an 'int' array with a size of 2 using numpy method

  arr = np.empty(2,dtype=int)
• creating array using numpy method (syntax)

  arr = np.empty(shape,dtype=datatype,order='C')
• create 'int' array with elements [1,2,3]

  arr = arr.array('i',[1,2,3])
• create int 3x3 array using numpy method

  arr = np.empty([3,3],dtype=int)
• declare a series (series as variable)with 1,2,3 values and a,b,c as index

  series = pd.Series([1,2,3],index=['a','b','c'])
• dict = {'a':1,'b':2,'c':3}

  create a series using this dictionary (use series as variable)

  series = pd.Series(dict)
• dict = {'a':1,'b':2,'c':3}

  create series only key 'a' and 'c' only (use series as variable)

  series = pd.Series(dict,index=['a','c'])
• dict = {'a':1,'b':2,'c':3,'d':4,'e':5}series = pd.Series(dict)

  print only the key 'a' to 'c' using slicing

  print(series['a':'c'])
• csvfile.csv

  read this csv file (use data as variable)

  data = pd.read_csv("csvfile.csv")
• create a dictionary ('name' 1.mike 2.irish 3.mari) ('gender' 1.male 2.female 3.female) then,

  create a dataframe using that dictionary (use data as variable)

  dict = {'name':['mike','irish','mari'],'gender':['male',female','female'])

  data = pd.DataFrame(dict)
• create a 2x2 numpy array ('a','b')(1,2) (use arr as variable)

  create a dictionary with a key ('letter' and 'number') with the values of the numpy array (use dict as variable)

  create a dataframe using that dictionary (use data as variable)

  arr = np.array([['a','b'],[1,2])

  dict = {'letter':arr[0],'number':arr[1])

  data = pd.DataFrame(dict)
• "data" transposing dataframe or csv file data (use data_trans as variable to hold the "data")

  data_trans = data.transpose()
• head() method returns the headers and specified number of row, starting from the top. One of the most used method for getting a quick overview of the dataframe.
• tail() method for viewing the last rows of the dataframe. tail() method returns the headers and specified numbers of rows, starting from the bottom.
• df.info() gives you more information about the dataset.
• arr = np.arange(1,10+1)

  [1,2,3,4,5,6,7,8,9,10]
• arr = np.arange(8)

  make a 2x4 array using .reshape()

  arr = np.arange(8).reshape(2,4)
• arr = np.arange(8).reshape(4,2)

  [[0,1],

  [2,3],

  [4,5],

  [6,7]]