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])
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])
print(thislist[-4:-1])
•thislist =
["apple", "banana", "cherry"]
if "apple" in thislist:
print("Yes, 'apple' is in the fruits list")
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)
tropical = ["mango", "pineapple", "papaya"]
thislist.extend(tropical)
print(thislist)
thisdict = {
"brand": "Ford",
"model": "Mustang",
"year": 1964,
"year": 2020
}
print(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)
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
... 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
>>> 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())
df = pd.read_csv('data.csv')
print(df.to_string())
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
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
Comments
Post a Comment