Input Jam

I’m learning the Pandas library for Python. Here are some note.

Series

General form:
- pandas.Series(data=None, index=None)
Example:
- pandas.Series(['big', 'small', 'red', 42])
Example:
- pandas.Series(data=['big', 'small', 'red', 42], index=['desired_wealth', 'actual_wealth', 'status', 'answer'])
Getting data out of a Series:
- x = series['status']
Getting multiple data out:
- y = series[['status','answer']]
Get a sub-series, where the value is transformed (in this case: multiplied by 3). Returns a series:
- z = seriesName * 3
Get sub-series from a series where value meets a condition (in this case: != 3). Returns a series:
- zz = seriesName[seriesName != 3]

DataFrame

General form:
- pandas.DataFrame(data=None)
Example:
- pandas.DataFrame({ 'year': [2010, 2011, 2012, 2011, 2012, 2010, 2011, 2012], 'team': ['Bears', 'Bears', 'Bears', 'Packers', 'Packers', 'Lions', 'Lions', 'Lions'], 'wins': [11, 8, 10, 15, 11, 6, 10, 4], 'losses': [5, 8, 6, 1, 5, 10, 6, 12]})
Functions/selectors you can call on a DataFrame named d:
- d.dtypes - what is the data type of each column
- d.describe() - show some aggregated metrics about the values in the data frame
- d.head() - show the first few rows
- d.tail() - show the last few rows
- d.head(2) - show the firs 2 rows
Load a CSV file:
- d = pandas.read_csv('./file1.csv')

Create a data frame from a literal:

  my_cars = ['Prius', 'Corolla', 'Matchbox']
  horsepower = [120, 130, 0]
  colors = ['gold', 'silver', 'rainbow']
  t = DataFrame({
      'my_cars':Series(my_cars),
      'horsepower':Series(horsepower),
      'color':Series(colors)
      })

Create a data frame from a literal, with an index:

  d = DataFrame({
      'one':Series([1, 2, 3], index=['a','b','c']),
      'two':Series([1, 2, 3], index=['a','b','c'])
      })

Get a column from a data frame:
- Treat it like a dictionary:
  - x = d['columnName']
- Or treat it like a field:
  - x = d.columnName
Get multiple columns from a data frame:
- x = d[['colName1', 'colName2']]
Get a row from a data frame:
- d.loc[index]
- d.loc[3]
- d.loc['namedIndex']
Select rows from a data frame that meet a criteria:
- d[d['horsepower'] >= 120]
Get rows 3-5 (Note: Last number is strictly less than)
- d[3:6]
Select and project (one column)
- d[‘color’][d[‘horsepower’] >= 120]
Select and project (multiple columns)
- d[[‘color’,’horsepower’]][d[‘horsepower’] >= 120]
  - The boolean test return all the indices meeting that criteria. Then those indices are used to select the rows from the color+horsepower intermediate data frame.
Apply a function to every column:
- d.apply(numpy.median)
Apply a function to each value one column:
- d['colName'].map(lambda x: x+1)
Apply a function to every value in every row and column
- d['colName'].applymap(lambda x: x+1)
Apply an aggregate function to a column:
- numpy.mean(d['horsepower'])

Matrix Multiply Example From Udacity Course

import numpy
from pandas import DataFrame, Series

def numpy_dot():
    '''
    4 points for gold, 2 points per silver, one point per bronze.
    Using the numpy.dot function, return a new dataframe with
        a) a column called 'country_name' with the country name
        b) a column called 'points' with the total number of points the country earned.
    '''
    countries = ['Russian Fed.', 'Norway', 'Canada', 'United States',
                 'Netherlands', 'Germany', 'Switzerland', 'Belarus',
                 'Austria', 'France', 'Poland', 'China', 'Korea', 
                 'Sweden', 'Czech Republic', 'Slovenia', 'Japan',
                 'Finland', 'Great Britain', 'Ukraine', 'Slovakia',
                 'Italy', 'Latvia', 'Australia', 'Croatia', 'Kazakhstan']
    gold = [13, 11, 10, 9, 8, 8, 6, 5, 4, 4, 4, 3, 3, 2, 2, 2, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0]
    silver = [11, 5, 10, 7, 7, 6, 3, 0, 8, 4, 1, 4, 3, 7, 4, 2, 4, 3, 1, 0, 0, 2, 2, 2, 1, 0]
    bronze = [9, 10, 5, 12, 9, 5, 2, 1, 5, 7, 1, 2, 2, 6, 2, 4, 3, 1, 2, 1, 0, 6, 2, 1, 0, 1]

    d = DataFrame({
        'country_name':Series(countries), 
        'gold':Series(gold),
        'silver':Series(silver),
        'bronze':Series(bronze)
        })[['gold','silver','bronze']]
    v = numpy.dot(d,[4,2,1])
    return DataFrame({
        'country_name':Series(countries),
        'points':Series(v)  })

</code>

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Pandas (Python)

Series

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Matrix Multiply Example From Udacity Course