datacamp: notes for python

Intro to python for data science

Boolean

True and False, First letter should be uppercase.

Delete list elements

Finally, you can also remove elements from your list. You can do this with the del statement:

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x = ["a", "b", "c", "d"]
del(x[1])

More explicit copy

Do list copy with list() or by using [:].

Functions

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# Create variables var1 and var2
var1 = [1, 2, 3, 4]
var2 = True
# Print out type of var1
print(type(var1))
# Print out length of var1
print(len(var1))
# Convert var2 to an integer: out2
out2 = int(var2)

Ironically, you have to ask for information about a function with another function: help()

List methods

  • upper()

str.upper(): Notice from the printouts that the upper() method does not change the object it is called on.

  • index(), to get the index of the first element of a list that matches its input and

  • count(), to get the number of times an element appears in a list.

  • append(), that adds an element to the list it is called on,

  • remove(), that removes the first element of a list that matches the input, and
  • reverse(), that reverses the order of the elements in the list it is called on.

Numpy

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import numpy as np

Use np.array() to create a numpy array 

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In [1]: np.array([True, 1, 2]) + np.array([3, 4, False]) 
## True is converted to 1, False is converted to 0
... 
Out[1]: array([4, 5, 2])

numpy arrays slicing including the start element, excluding the end element.

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# Print out sub-array of np_height: index 100 up to and including index 110
print(np_height[100:111])

Intermediate Python for data science

Matplotlib.pyplot

Build a histogram (1)

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# Create histogram of life_exp data
plt.hist(life_exp)
# Display histogram
plt.show()

Build a histogram (2): bins

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# Build histogram with 5 bins
plt.hist(life_exp, bins = 5)
# Show and clean up plot
plt.show()
plt.clf()
# Build histogram with 20 bins
plt.hist(life_exp, bins = 20)
# Show and clean up again
plt.show()
plt.clf()

Labels

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# Basic scatter plot, log scale
plt.scatter(gdp_cap, life_exp)
plt.xscale('log') 
# Strings
xlab = 'GDP per Capita [in USD]'
ylab = 'Life Expectancy [in years]'
title = 'World Development in 2007'
# Add axis labels
plt.xlabel(xlab)
plt.ylabel(ylab)
# Add title
plt.title(title)
# After customizing, display the plot
plt.show()

Additional Customizations

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# Scatter plot
plt.scatter(x = gdp_cap, y = life_exp, s = np.array(pop) * 2, c = col, alpha = 0.8)
# Previous customizations
plt.xscale('log') 
plt.xlabel('GDP per Capita [in USD]')
plt.ylabel('Life Expectancy [in years]')
plt.title('World Development in 2007')
plt.xticks([1000,10000,100000], ['1k','10k','100k'])
# Additional customizations
plt.text(1550, 71, 'India')
plt.text(5700, 80, 'China')
# Add grid() call
plt.grid(True)
# Show the plot
plt.show()

Dictionary manipulation

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# Definition of dictionary
europe = {'spain':'madrid', 'france':'paris', 'germany':'berlin', 'norway':'oslo' }
# Add italy to europe
europe['italy'] = 'rome'
# Print out italy in europe
print('italy' in europe)
# Add poland to europe
europe['poland'] = 'warsaw'
# Print europe
print(europe)
# Definition of dictionary
europe = {'spain':'madrid', 'france':'paris', 'germany':'bonn',
          'norway':'oslo', 'italy':'rome', 'poland':'warsaw',
          'australia':'vienna' }
# Update capital of germany
europe['germany'] = 'berlin'
# Remove australia
del(europe['australia'])
# Print europe
print(europe)
# Dictionary of dictionaries
europe = { 'spain': { 'capital':'madrid', 'population':46.77 },
           'france': { 'capital':'paris', 'population':66.03 },
           'germany': { 'capital':'berlin', 'population':80.62 },
           'norway': { 'capital':'oslo', 'population':5.084 } }
# Print out the capital of France
print(europe['france']['capital'])
# Create sub-dictionary data
data = {}
data['capital'] = 'rome'
data['population'] = 59.83
# Add data to europe under key 'italy'
europe['italy'] = data
# Print europe
print(europe)

Pandas

Dictionary to DataFrame

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# Pre-defined lists
names = ['United States', 'Australia', 'Japan', 'India', 'Russia', 'Morocco', 'Egypt']
dr =  [True, False, False, False, True, True, True]
cpc = [809, 731, 588, 18, 200, 70, 45]
# Import pandas as pd
import pandas as pd
# Create dictionary my_dict with three key:value pairs: my_dict
my_dict = {'country': names,'drives_right':dr,'cars_per_cap':cpc}
# Build a DataFrame cars from my_dict: cars
cars = pd.DataFrame(my_dict)
# Print cars
print(cars)
import pandas as pd
# Build cars DataFrame
names = ['United States', 'Australia', 'Japan', 'India', 'Russia', 'Morocco', 'Egypt']
dr =  [True, False, False, False, True, True, True]
cpc = [809, 731, 588, 18, 200, 70, 45]
dict = { 'country':names, 'drives_right':dr, 'cars_per_cap':cpc }
cars = pd.DataFrame(dict)
print(cars)
# Definition of row_labels
row_labels = ['US', 'AUS', 'JAP', 'IN', 'RU', 'MOR', 'EG']
# Specify row labels of cars
cars.index = row_labels
# Print cars again
print(cars)

CSV to DataFrame

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# Import pandas as pd
import pandas as pd
# Import the cars.csv data: cars
cars = pd.read_csv('cars.csv')
# Print out cars
print(cars)
# Import pandas as pd
import pandas as pd
# Fix import by including index_col
cars = pd.read_csv('cars.csv', index_col = 0)
# Print out cars
print(cars)

Square brackets

  • The single bracket version gives a Pandas Series, the double bracket version gives a Pandas DataFrame.
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# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)
# Print out country column as Pandas Series
print(cars['country'])
# Print out country column as Pandas DataFrame
print(cars[['country']])
# Print out DataFrame with country and drives_right columns
print(cars[['country','drives_right']])
  • You can only select rows using square brackets if you specify a slice, like 0:4. Also, you’re using the integer indexes of the rows here, not the row labels!
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# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)
# Print out first 3 observations
print(cars[0:3])
# Print out fourth, fifth and sixth observation
print(cars[3:6])

loc and iloc

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# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)
# Print out observation for Japan
print(cars.loc['JAP'])
print(cars.iloc[2])
# Print out observations for Australia and Egypt
print(cars.loc[['AUS','EG']])
print(cars.iloc[[1,6]])
# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)
# Print out drives_right value of Morocco
print(cars.loc['MOR','drives_right'])
# Print sub-DataFrame
print(cars.loc[['RU','MOR'],['country','drives_right']])
# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)
# Print out drives_right column as Series
print(cars.loc[:,'drives_right'])
print(cars.iloc[:,2])
# Print out drives_right column as DataFrame
print(cars.loc[:,['drives_right']])
print(cars.iloc[:,[2]])
# Print out cars_per_cap and drives_right as DataFrame
print(cars.loc[:,['cars_per_cap','drives_right']])
print(cars.iloc[:,[0,2]])

Logic, Control Flow and Filtering

Comparison

Remember that for string comparison, Python determines the relationship based on alphabetical order.

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# Comparison of integers
x = -3 * 6
print (x >= -10)
# Comparison of strings
y = "test"
print("test" <= y)
# Comparison of booleans
print(True > False)
### output:
###     False
###     True
###     True
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    缺失模块。
    1、在博客根目录（注意不是yilia根目录）执行以下命令：
    npm i hexo-generator-json-content --save

    2、在根目录_config.yml里添加配置： 

      jsonContent:
    meta: false
    pages: false
    posts:
      title: true
      date: true
      path: true
      text: true
      raw: false
      content: false
      slug: false
      updated: false
      comments: false
      link: false
      permalink: false
      excerpt: false
      categories: false
      tags: true

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