Data science class -004

•    Data Science - steps
•    Data Sizing
•    Examples
•    Lists
•    Dictionaries
•    Dataframes

 

 

 

 

 

 

•thislist = ["apple", "banana", "cherry", "orange", "kiwi", "melon", "mango"]
print(thislist[2:5]) 

 

•This example returns the items from "orange" (-4) to, but NOT including "mango" (-1):

•thislist = ["apple", "banana", "cherry", "orange", "kiwi", "melon", "mango"]
print(thislist[-4:-1]) 

 

•https://www.w3schools.com/python/python_lists_access.asp

 

•thislist = ["apple", "banana", "cherry"]
if "apple" in thislist:
  print("Yes, 'apple' is in the fruits list")

  

•thislist = ["apple", "banana", "cherry"]
tropical = ["mango", "pineapple", "papaya"]
thislist.extend(tropical)
print(thislist)

 

thisdict = {
  "brand": "Ford",
  "model": "Mustang",
  "year": 1964,
  "year": 2020
}
print(thisdict)

  

 

•Pandas is a Python library used for working with data sets.

•It has functions for analyzing, cleaning, exploring, and manipulating data.

•The name "Pandas" has a reference to both "Panel Data", and "Python Data Analysis" and was created by Wes McKinney in 2008.

•import pandas as pd

data = {
  "calories": [420, 380, 390],
  "duration": [50, 40, 45]
}

#load data into a DataFrame object:
df = pd.DataFrame(data)

print(df) 

 

>>> for x in thisdict:
...     print(thisdict[x])
...
Ford
Mustang
2015
red

{'brand': 'Ford', 'model': 'Mustang', 'year': 2015, 'color': 'red'}
>>> for x in thisdict:
...     print(x)
...
brand
model
year
color

Dara frames

import pandas as pd

data = {
  "calories": [420, 380, 390],
  "duration": [50, 40, 45]
}

#load data into a DataFrame object:
df = pd.DataFrame(data)

print(df) 

 

CSV

 

The number of rows returned is defined in Pandas option settings.

You can check your system's maximum rows with the pd.options.display.max_rows statement.

 

 

import pandas as pd

df = pd.read_csv('data.csv')

print(df.to_string()) 

import pandas as pd
print(pd.options.display.max_rows) 

Duration,Pulse,Maxpulse,Calories
60,110,130,409.1
60,117,145,479.0
60,103,135,340.0
45,109,175,282.4
45,117,148,406.0
60,102,127,300.5
60,110,136,374.0
45,104,134,253.3
30,109,133,195.1
60,98,124,269.0
60,103,147,329.3
60,100,120,250.7
60,106,128,345.3
60,104,132,379.3
60,98,123,275.0
60,98,120,215.2
60,100,120,300.0
45,90,112,
60,103,123,323.0
45,97,125,243.0
60,108,131,364.2
45,100,119,282.0
60,130,101,300.0
45,105,132,246.0
60,102,126,334.5
60,100,120,250.0
60,92,118,241.0
60,103,132
60,100,132,280.0
60,102,129,380.3
60,92,115,243.0
45,90,112,180.1
60,101,124,299.0
60,93,113,223.0
60,107,136,361.0
60,114,140,415.0
60,102,127,300.5
60,100,120,300.1
60,100,120,300.0
45,104,129,266.0
45,90,112,180.1
60,98,126,286.0
60,100,122,329.4
60,111,138,400.0
60,111,131,397.0
60,99,119,273.0
60,109,153,387.6
45,111,136,300.0
45,108,129,298.0
60,111,139,397.6
60,107,136,380.2
80,123,146,643.1
60,106,130,263.0
60,118,151,486.0
30,136,175,238.0
60,121,146,450.7
60,118,121,413.0
45,115,144,305.0
20,153,172,226.4
45,123,152,321.0
210,108,160,1376.0
160,110,137,1034.4
160,109,135,853.0
45,118,141,341.0
20,110,130,131.4
180,90,130,800.4
150,105,135,873.4
150,107,130,816.0
20,106,136,110.4
300,108,143,1500.2
150,97,129,1115.0
60,109,153,387.6
90,100,127,700.0
150,97,127,953.2
45,114,146,304.0
90,98,125,563.2
45,105,134,251.0
45,110,141,300.0
120,100,130,500.4
270,100,131,1729.0
30,159,182,319.2
45,149,169,344.0
30,103,139,151.1
120,100,130,500.0
45,100,120,225.3
30,151,170,300.1
45,102,136,234.0
120,100,157,1000.1
45,129,103,242.0
20,83,107,50.3
180,101,127,600.1
45,107,137,
30,90,107,105.3
15,80,100,50.5
20,150,171,127.4
20,151,168,229.4
30,95,128,128.2
25,152,168,244.2
30,109,131,188.2
90,93,124,604.1
20,95,112,77.7
90,90,110,500.0
90,90,100,500.0
90,90,100,500.4
30,92,108,92.7
30,93,128,124.0
180,90,120,800.3
30,90,120,86.2
90,90,120,500.3
210,137,184,1860.4
60,102,124,325.2
45,107,124,275.0
15,124,139,124.2
45,100,120,225.3
60,108,131,367.6
60,108,151,351.7
60,116,141,443.0
60,97,122,277.4
60,105,125,
60,103,124,332.7
30,112,137,193.9
45,100,120,100.7
60,119,169,336.7
60,107,127,344.9
60,111,151,368.5
60,98,122,271.0
60,97,124,275.3
60,109,127,382.0
90,99,125,466.4
60,114,151,384.0
60,104,134,342.5
60,107,138,357.5
60,103,133,335.0
60,106,132,327.5
60,103,136,339.0
20,136,156,189.0
45,117,143,317.7
45,115,137,318.0
45,113,138,308.0
20,141,162,222.4
60,108,135,390.0
60,97,127,
45,100,120,250.4
45,122,149,335.4
60,136,170,470.2
45,106,126,270.8
60,107,136,400.0
60,112,146,361.9
30,103,127,185.0
60,110,150,409.4
60,106,134,343.0
60,109,129,353.2
60,109,138,374.0
30,150,167,275.8
60,105,128,328.0
60,111,151,368.5
60,97,131,270.4
60,100,120,270.4
60,114,150,382.8
30,80,120,240.9
30,85,120,250.4
45,90,130,260.4
45,95,130,270.0
45,100,140,280.9
60,105,140,290.8
60,110,145,300.4
60,115,145,310.2
75,120,150,320.4
75,125,150,330.4