优达学城Numpy与Pandas笔记
2024-06-07 05:39:26
此篇为优达学城数据分析入门第二课笔记网址
基本操作
import numpy as np# First 20 countries with employment data
countries = np.array(['Afghanistan', 'Albania', 'Algeria', 'Angola', 'Argentina','Armenia', 'Australia', 'Austria', 'Azerbaijan', 'Bahamas','Bahrain', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium','Belize', 'Benin', 'Bhutan', 'Bolivia','Bosnia and Herzegovina'
])# Employment data in 2007 for those 20 countries
employment = np.array([55.70000076, 51.40000153, 50.5 , 75.69999695,58.40000153, 40.09999847, 61.5 , 57.09999847,60.90000153, 66.59999847, 60.40000153, 68.09999847,66.90000153, 53.40000153, 48.59999847, 56.79999924,71.59999847, 58.40000153, 70.40000153, 41.20000076
])# Change False to True for each block of code to see what it does# Accessing elements
if True:print(countries[0])print(countries[3])# Slicing
if False:print(countries[0:3])print(countries[:3])print(countries[17:])print(countries[:])# Element types
if False:print(countries.dtype)print(employment.dtype)print(np.array([0, 1, 2, 3]).dtype)print(np.array([1.0, 1.5, 2.0, 2.5]).dtype)print(np.array([True, False, True]).dtype)print(np.array(['AL', 'AK', 'AZ', 'AR', 'CA']).dtype)# Looping
if False:for country in countries:print('Examining country {}'.format(country))for i in range(len(countries)):country = countries[i]country_employment = employment[i]print('Country {} has employment {}'.format(country,country_employment))# Numpy functions
if False:print(employment.mean())print(employment.std())print(employment.max())print(employment.sum())def max_employment(countries, employment):'''Fill in this function to return the name of the countrywith the highest employment in the given employmentdata, and the employment in that country.'''i = employment.argmax()max_country = countries[i] # Replace this with your codemax_value = employment[i] # Replace this with your codereturn (max_country, max_value)Afghanistan
Angola
运算
import numpy as np# Change False to True for each block of code to see what it does# Arithmetic operations between 2 NumPy arrays
if False:a = np.array([1, 2, 3, 4])b = np.array([1, 2, 1, 2])print (a + b)print (a - b)print (a * b)print (a / b)print (a ** b)# Arithmetic operations between a NumPy array and a single number
if False:a = np.array([1, 2, 3, 4])b = 2print (a + b)print (a - b)print (a * b)print (a / b)print (a ** b)# Logical operations with NumPy arrays
if False:a = np.array([True, True, False, False])b = np.array([True, False, True, False])print (a & b)print (a | b)print (~a)print (a & True)print (a & False)print (a | True)print (a | False)# Comparison operations between 2 NumPy Arrays
if False:a = np.array([1, 2, 3, 4, 5])b = np.array([5, 4, 3, 2, 1])print (a > b)print (a >= b)print (a < b)print (a <= b)print (a == b)print (a != b)# Comparison operations between a NumPy array and a single number
if False:a = np.array([1, 2, 3, 4])b = 2print (a > b)print (a >= b)print (a < b)print (a <= b)print (a == b)print (a != b)# First 20 countries with school completion data
countries = np.array(['Algeria', 'Argentina', 'Armenia', 'Aruba', 'Austria','Azerbaijan','Bahamas', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Bolivia','Botswana', 'Brunei', 'Bulgaria', 'Burkina Faso', 'Burundi','Cambodia', 'Cameroon', 'Cape Verde'
])# Female school completion rate in 2007 for those 20 countries
female_completion = np.array([97.35583, 104.62379, 103.02998, 95.14321, 103.69019,98.49185, 100.88828, 95.43974, 92.11484, 91.54804,95.98029, 98.22902, 96.12179, 119.28105, 97.84627,29.07386, 38.41644, 90.70509, 51.7478 , 95.45072
])# Male school completion rate in 2007 for those 20 countries
male_completion = np.array([95.47622, 100.66476, 99.7926 , 91.48936, 103.22096,97.80458, 103.81398, 88.11736, 93.55611, 87.76347,102.45714, 98.73953, 92.22388, 115.3892 , 98.70502,37.00692, 45.39401, 91.22084, 62.42028, 90.66958
])def overall_completion_rate(female_completion, male_completion):'''Fill in this function to return a NumPy array containing the overallschool completion rate for each country. The arguments are NumPyarrays giving the female and male completion of each country inthe same order.'''return female_completion + male_completionsum_completion= overall_completion_rate(female_completion, male_completion)
print(sum_completion)
[ 192.83205 205.28855 202.82258 186.63257 206.91115 196.29643204.70226 183.5571 185.67095 179.31151 198.43743 196.96855188.34567 234.67025 196.55129 66.08078 83.81045 181.92593114.16808 186.1203 ]
归一化
import numpy as np# First 20 countries with employment data
countries = np.array(['Afghanistan', 'Albania', 'Algeria', 'Angola', 'Argentina','Armenia', 'Australia', 'Austria', 'Azerbaijan', 'Bahamas','Bahrain', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium','Belize', 'Benin', 'Bhutan', 'Bolivia','Bosnia and Herzegovina'
])# Employment data in 2007 for those 20 countries
employment = np.array([55.70000076, 51.40000153, 50.5 , 75.69999695,58.40000153, 40.09999847, 61.5 , 57.09999847,60.90000153, 66.59999847, 60.40000153, 68.09999847,66.90000153, 53.40000153, 48.59999847, 56.79999924,71.59999847, 58.40000153, 70.40000153, 41.20000076
])# Change this country name to change what country will be printed when you
# click "Test Run". Your function will be called to determine the standardized
# score for this country for each of the given 5 Gapminder variables in 2007.
# The possible country names are available in the Downloadables section.country_name = 'United States'def standardize_data(values):'''Fill in this function to return a standardized version of the given values,which will be in a NumPy array. Each value should be translated into thenumber of standard deviations that value is away from the mean of the data.(A positive number indicates a value higher than the mean, and a negativenumber indicates a value lower than the mean.)'''return (values-values.mean())/values.std()standardize_data(employment)array([-0.31965231, -0.780123 , -0.87650077, 1.82207181, -0.03051941,-1.99019768, 0.30144772, -0.16973184, 0.23719615, 0.84758731,0.18365304, 1.00821665, 0.87971351, -0.56595055, -1.07996476,-0.20185762, 1.38301845, -0.03051941, 1.2545153 , -1.87240259])
import numpy as np# Change False to True for each block of code to see what it does# Using index arrays
if False:a = np.array([1, 2, 3, 4])b = np.array([True, True, False, False])# print a[b]
# print a[np.array([True, False, True, False])]# Creating the index array using vectorized operations
if False:a = np.array([1, 2, 3, 2, 1])b = (a >= 2)# print a[b]
# print a[a >= 2]# Creating the index array using vectorized operations on another array
if False:a = np.array([1, 2, 3, 4, 5])b = np.array([1, 2, 3, 2, 1])# print b == 2
# print a[b == 2]def mean_time_for_paid_students(time_spent, days_to_cancel):'''Fill in this function to calculate the mean time spent in the classroomfor students who stayed enrolled at least (greater than or equal to) 7 days.Unlike in Lesson 1, you can assume that days_to_cancel will contain onlyintegers (there are no students who have not canceled yet).The arguments are NumPy arrays. time_spent contains the amount of time spentin the classroom for each student, and days_to_cancel contains the numberof days until each student cancel. The data is given in the same orderin both arrays.'''st=time_spent[days_to_cancel >= 7]return st.mean()# Time spent in the classroom in the first week for 20 students
time_spent = np.array([12.89697233, 0. , 64.55043217, 0. ,24.2315615 , 39.991625 , 0. , 0. ,147.20683783, 0. , 0. , 0. ,45.18261617, 157.60454283, 133.2434615 , 52.85000767,0. , 54.9204785 , 26.78142417, 0.
])# Days to cancel for 20 students
days_to_cancel = np.array([4, 5, 37, 3, 12, 4, 35, 38, 5, 37, 3, 3, 68,38, 98, 2, 249, 2, 127, 35
])mean_time_for_paid_students(time_spent, days_to_cancel)41.054003485454537
numpy的+=与切片需要注意，类似指针去理解
Pandas Series
import pandas as pdcountries = ['Albania', 'Algeria', 'Andorra', 'Angola', 'Antigua and Barbuda','Argentina', 'Armenia', 'Australia', 'Austria', 'Azerbaijan','Bahamas', 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus','Belgium', 'Belize', 'Benin', 'Bhutan', 'Bolivia']life_expectancy_values = [74.7, 75. , 83.4, 57.6, 74.6, 75.4, 72.3, 81.5, 80.2,70.3, 72.1, 76.4, 68.1, 75.2, 69.8, 79.4, 70.8, 62.7,67.3, 70.6]gdp_values = [ 1681.61390973, 2155.48523109, 21495.80508273, 562.98768478,13495.1274663 , 9388.68852258, 1424.19056199, 24765.54890176,27036.48733192, 1945.63754911, 21721.61840978, 13373.21993972,483.97086804, 9783.98417323, 2253.46411147, 25034.66692293,3680.91642923, 366.04496652, 1175.92638695, 1132.21387981]# Life expectancy and gdp data in 2007 for 20 countries
life_expectancy = pd.Series(life_expectancy_values)
gdp = pd.Series(gdp_values)# Change False to True for each block of code to see what it does# Accessing elements and slicing
# if False:
# print life_expectancy[0]
# print gdp[3:6]# Looping
if False:for country_life_expectancy in life_expectancy:print('Examining life expectancy {}'.format(country_life_expectancy))# Pandas functions
# if False:
# print life_expectancy.mean()
# print life_expectancy.std()
# print gdp.max()
# print gdp.sum()# Vectorized operations and index arrays
if False:a = pd.Series([1, 2, 3, 4])b = pd.Series([1, 2, 1, 2])print(a + b)print(a * 2)print(a >= 3)print(a[a >= 3])def variable_correlation(life_expectancy, gdp):'''Fill in this function to calculate the number of data points for whichthe directions of variable1 and variable2 relative to the mean are thesame, and the number of data points for which they are different.Direction here means whether each value is above or below its mean.You can classify cases where the value is equal to the mean for one orboth variables however you like.Each argument will be a Pandas series.For example, if the inputs were pd.Series([1, 2, 3, 4]) andpd.Series([4, 5, 6, 7]), then the output would be (4, 0).This is because 1 and 4 are both below their means, 2 and 5 are bothbelow, 3 and 6 are both above, and 4 and 7 are both above.On the other hand, if the inputs were pd.Series([1, 2, 3, 4]) andpd.Series([7, 6, 5, 4]), then the output would be (0, 4).This is because 1 is below its mean but 7 is above its mean, andso on.'''life_expectancy_values_dir = ((life_expectancy-life_expectancy.mean()) > 0)
# print(life_expectancy_values_dir)gdp_values_dir = ((gdp-gdp.mean()) > 0)
# print(gdp_values_dir)sum_dir = (life_expectancy_values_dir == gdp_values_dir)
# print(sum_dir)sum_dir_1 = (sum_dir == 1)sum_dir_0_2 = (sum_dir != 1)#len(sum_dir_1)-...num_same_direction = sum_dir_1.sum() # Replace this with your codenum_different_direction = sum_dir_0_2.sum() # Replace this with your codereturn (num_same_direction, num_different_direction)variable_correlation(life_expectancy, gdp)(17, 3)
带索引的pandas
import pandas as pdcountries = ['Afghanistan', 'Albania', 'Algeria', 'Angola','Argentina', 'Armenia', 'Australia', 'Austria','Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh','Barbados', 'Belarus', 'Belgium', 'Belize','Benin', 'Bhutan', 'Bolivia', 'Bosnia and Herzegovina',
]employment_values = [55.70000076, 51.40000153, 50.5 , 75.69999695,58.40000153, 40.09999847, 61.5 , 57.09999847,60.90000153, 66.59999847, 60.40000153, 68.09999847,66.90000153, 53.40000153, 48.59999847, 56.79999924,71.59999847, 58.40000153, 70.40000153, 41.20000076,
]# Employment data in 2007 for 20 countries
employment = pd.Series(employment_values, index=countries)def max_employment(employment):'''Fill in this function to return the name of the countrywith the highest employment in the given employmentdata, and the employment in that country.The input will be a Pandas series where the valuesare employment and the index is country names.Try using the Pandas idxmax() function. Documention canbe found here:http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.idxmax.html'''max_country = employment.argmax() # Replace this with your codemax_value = employment.loc[max_country] # Replace this with your codereturn (max_country, max_value)print(max_employment(employment) )
('Angola', 75.699996949999999)
import pandas as pd# Change False to True for each block of code to see what it does# Addition when indexes are the same
if True:s1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])s2 = pd.Series([10, 20, 30, 40], index=['a', 'b', 'c', 'd'])print(s1 + s2)# Indexes have same elements in a different order
if True:s1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])s2 = pd.Series([10, 20, 30, 40], index=['b', 'd', 'a', 'c'])print(s1 + s2)# Indexes overlap, but do not have exactly the same elements
if True:s1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])s2 = pd.Series([10, 20, 30, 40], index=['c', 'd', 'e', 'f'])print(s1 + s2)sum = s1 + s2print(sum.dropna())
# Indexes do not overlap
if True:s1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])s2 = pd.Series([10, 20, 30, 40], index=['e', 'f', 'g', 'h'])print(s1 + s2)print(s1.add(s2, fill_value=0))
a 11
b 22
c 33
d 44
dtype: int64
a 31
b 12
c 43
d 24
dtype: int64
a NaN
b NaN
c 13.0
d 24.0
e NaN
f NaN
dtype: float64
c 13.0
d 24.0
dtype: float64
a NaN
b NaN
c NaN
d NaN
e NaN
f NaN
g NaN
h NaN
dtype: float64
a 1.0
b 2.0
c 3.0
d 4.0
e 10.0
f 20.0
g 30.0
h 40.0
dtype: float64
import pandas as pd# Change False to True to see what the following block of code does# Example pandas apply() usage (although this could have been done
# without apply() using vectorized operations)
if False:s = pd.Series([1, 2, 3, 4, 5])def add_one(x):return x + 1print(s.apply(add_one))names = pd.Series(['Andre Agassi','Barry Bonds','Christopher Columbus','Daniel Defoe','Emilio Estevez','Fred Flintstone','Greta Garbo','Humbert Humbert','Ivan Ilych','James Joyce','Keira Knightley','Lois Lane','Mike Myers','Nick Nolte','Ozzy Osbourne','Pablo Picasso','Quirinus Quirrell','Rachael Ray','Susan Sarandon','Tina Turner','Ugueth Urbina','Vince Vaughn','Woodrow Wilson','Yoji Yamada','Zinedine Zidane'
])def reverse_name(name):split_name = name.split(" ")firstname = split_name[0]secondname = split_name[1]return firstname+","+secondname# print(reverse_name(names[0]))def reverse_names(names):'''Fill in this function to return a new series where each namein the input series has been transformed from the format"Firstname Lastname" to "Lastname, FirstName".Try to use the Pandas apply() function rather than a loop.'''return names.apply(reverse_name)print(reverse_names(names))0 Andre,Agassi
1 Barry,Bonds
2 Christopher,Columbus
3 Daniel,Defoe
4 Emilio,Estevez
5 Fred,Flintstone
6 Greta,Garbo
7 Humbert,Humbert
8 Ivan,Ilych
9 James,Joyce
10 Keira,Knightley
11 Lois,Lane
12 Mike,Myers
13 Nick,Nolte
14 Ozzy,Osbourne
15 Pablo,Picasso
16 Quirinus,Quirrell
17 Rachael,Ray
18 Susan,Sarandon
19 Tina,Turner
20 Ugueth,Urbina
21 Vince,Vaughn
22 Woodrow,Wilson
23 Yoji,Yamada
24 Zinedine,Zidane
dtype: object
import pandas as pd
import matplotlib.pyplot as plt
employment = pd.read_csv("employment-above-15.csv", index_col='Country')
employment_us = employment.loc['United States']
# %pylab inline
# employment_us.plt()优达学城Numpy与Pandas笔记相关推荐
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