• Python
• Lists
• Numpy

• dicts

langs_count = {}
col = df['lang'] # say col is a column from df dataframe
for entry in col:
    # If the language is in langs_count, add 1
    if entry in langs_count.keys():
        langs_count[entry] = langs_count[entry]+1;
    # Else add the language to langs_count, set the value to 1
    else:
        langs_count[entry] = 1

• pandasintro

# Extract column from DataFrame: col
col = df['lang']
print(list(df.columns.values)) # to print column names in pandas data frame
print(df.iloc[0]) # to see first row how it looks

• conditions

ends= np.array(ends)
print(len(ends[ends>=60]))

• loops
• Dictionary = for key,val in my_dict.items():
• Numpy Array = for val in np.nditer(my_array):
• Data Frame = for lab, row in brics.iterrows() : sometimes we prefer apply function for creation of new columns

for lab, row in brics.iterrows() :
    brics.loc[lab, "name_length"] = len(row["country"])

brics["name_length"] = brics["country"].apply(len) 

columns check in data frame

• Functions
• Tuples & in Functions
• Functions in dataframs by Pandas
• Functions - variables in Local & global scope

    num = 3
    print(num)

def func2():
    global num
    double_num = num * 2
    num = 6
    print(double_num) 

• Nested Functions

# Define echo
def echo(n):
    """Return the inner_echo function."""
    def inner_echo(word1):
        """Concatenate n copies of word1."""
        echo_word = word1 * n
        return echo_word
    return inner_echo
twice = echo(2)
two = echo(2)('hello') # => we can also do currying
thrice = echo(3)
three = echo(3)('hello')
print(twice('hello'), thrice('hello')) #closures
print(two, three)

• Scopes

  • Local Scope
  • Enclosing Functions (nonlocal to change variable even in outer enclosing function level)
  • Global ( to change variable in global level from a function)
  • Built in

• Default & Flexible arguments

# default
def shout_echo(word1, echo=1, intense=False):
...
with_big_echo = shout_echo('Hey',5,True)
big_no_echo = shout_echo('Hey',intense=True)


# multiple at once
def gibberish(*args):
...
many_words = gibberish("luke", "leia", "han", "obi", "darth")


# need key values both from assignment
def report_status(**kwargs):
...
report_status(name="luke", affiliation="jedi", status="missing")

• Lambda functions

lambda item1,item2: item1+item2
lambda member: len(member)>6
lambda item: item+"!!!"

• error handling

  • try except

  try:
      col = df[col_name]
      for entry in col:
          if entry in cols_count.keys():
              cols_count[entry] += 1
          else:
              cols_count[entry] = 1
      return cols_count
  except:
      print('The DataFrame does not have a '+ col_name + ' column.')

  • raise ValueError

   # Raise a ValueError if col_name is NOT in DataFrame
  if col_name not in df.columns:
      raise ValueError('The DataFrame does not have a '+col_name+' column.')

  Python toolbox 2

  • user defined functions
  • iterators
  • List comprehensions
  • Generators

  Importing Data Python - 1

  • textfiles

  import numpy as np
  import matplotlib.pyplot as plt
  # Assign filename: file
  file = 'sea_slug.txt'
  
  # Import file: data
  data = np.loadtxt(file, delimiter='\t', dtype=str)

  • Flatfiles CSV

  import pandas as pd
  import numpy as np
  # Assign the filename: file
  file = 'titanic.csv'
  
  # Read the first 5 rows of the file into a DataFrame: data
  data = pd.read_csv(file,nrows=5,header=None).head()
  
  # Build a numpy array from the DataFrame: data_array
  data_array = np.array(data)

  • Pickle files

  import pickle
  with open('pickled_fruit.pkl', 'rb') as file:
      data = pickle.load(file)
  print(data)

  • Excel Spread sheets

  # Import pandas
  import pandas as pd
  
  # Assign spreadsheet filename: file
  file = 'battledeath.xlsx'
  
  # Load spreadsheet: xl
  xl = pd.ExcelFile(file)
  
  # Print sheet names
  print(xl.sheet_names)

  • SAS file

  import pandas as pd
  # before this ensure you have installed sas7bdat -  pip install sas7bdat
  from sas7bdat import SAS7BDAT
  with SAS7BDAT('sales.sas7bdat') as file:
      df_sas = file.to_data_frame()

  • Stata

  # Import pandas
  import pandas as pd
  
  # Load Stata file into a pandas DataFrame: df
  df = pd.read_stata('disarea.dta')
  
  # Print the head of the DataFrame df
  print(df.head())

  • Matlab file

  import scipy.io
  import matplotlib.pyplot as plt
  import numpy as np
  # Print the keys of the MATLAB dictionary
  mat = scipy.io.loadmat('albeck_gene_expression.mat')
  print(mat.keys())
  data = mat['CYratioCyt'][25, 5:]

  • HDF5

  import h5py
  # Get the HDF5 group: group
  data = h5py.File('LIGO_data.hdf5','r')
  group = data['strain']
  
  # Check out keys of group
  for key in group.keys():
      print(key)
  
  # Set variable equal to time series data: strain
  strain = data['strain']['Strain'].value
  print(strain[0:5])

  Importing Data Python - 1 DBs

  • Querying SQL type DB's

  from sqlalchemy import create_engine
  import pandas as pd
  engine= create_engine('sqlite:///Chinook.sqlite')
  #table_names = engine.table_names()
  con = engine.connect()
  rs = con.execute('SELECT * FROM Album')
  df = pd.DataFrame(rs.fetchall())
  con.close()
  print(df.head())

  • pandas way

  # Execute query and store records in DataFrame: df
  df = pd.read_sql_query('SELECT * FROM PlaylistTrack INNER JOIN Track ON PlaylistTrack.TrackId = Track.TrackId WHERE Milliseconds <250000', engine)
  
  # Print head of DataFrame
  print(df.head())