Python数据分析习题（基于pandas&numpy模块）（下）_请编写python代码,对appl公司股价数据进行下列操作

练习题目录

习题编号 内容 相应数据集

• 练习6 - 统计 探索风速数据 wind.data
• 练习7 - 可视化 探索泰坦尼克灾难数据 train.csv
• 练习8 - 创建数据框 探索Pokemon数据 练习中手动内置的数据
• 练习9 - 时间序列 探索Apple公司股价数据 Apple_stock.csv
• 练习10 - 删除数据 探索Iris纸鸢花数据 iris.csv

练习6-统计

探索风速数据

步骤1 导入必要的库

import pandas as pd
import datetime
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步骤2 从以下地址导入数据

path6 = "./exercise_data/wind.data"
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步骤3 将数据作存储并且设置前三列为合适的索引

import datetime
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data = pd.read_table(path6, sep = "\s+", parse_dates = [[0,1,2]]) 
data.head()
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步骤4 2061年？我们真的有这一年的数据？创建一个函数并用它去修复这个bug

def fix_century(x):
    year = x.year - 100 if x.year > 1989 else x.year
    return datetime.date(year, x.month, x.day)

# apply the function fix_century on the column and replace the values to the right ones
data['Yr_Mo_Dy'] = data['Yr_Mo_Dy'].apply(fix_century)

# data.info()
data.head()
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步骤5 将日期设为索引，注意数据类型，应该是datetime64[ns]

data["Yr_Mo_Dy"] = pd.to_datetime(data["Yr_Mo_Dy"])

# set 'Yr_Mo_Dy' as the index
data = data.set_index('Yr_Mo_Dy')

data.head()
# data.info()
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步骤6 对应每一个location，一共有多少数据值缺失

data.isnull().sum()
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RPT    6
VAL    3
ROS    2
KIL    5
SHA    2
BIR    0
DUB    3
CLA    2
MUL    3
CLO    1
BEL    0
MAL    4
dtype: int64
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步骤7 对应每一个location，一共有多少完整的数据值

data.shape[0] - data.isnull().sum()
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RPT    6568
VAL    6571
ROS    6572
KIL    6569
SHA    6572
BIR    6574
DUB    6571
CLA    6572
MUL    6571
CLO    6573
BEL    6574
MAL    6570
dtype: int64
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步骤8 对于全体数据，计算风速的平均值

data.mean().mean()
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10.227982360836924
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步骤9 创建一个名为loc_stats的数据框去计算并存储每个location的风速最小值，最大值，平均值和标准差

loc_stats = pd.DataFrame()

loc_stats['min'] = data.min() # min
loc_stats['max'] = data.max() # max 
loc_stats['mean'] = data.mean() # mean
loc_stats['std'] = data.std() # standard deviations

loc_stats
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步骤10 创建一个名为day_stats的数据框去计算并存储所有location的风速最小值，最大值，平均值和标准差

day_stats = pd.DataFrame()

# this time we determine axis equals to one so it gets each row.
day_stats['min'] = data.min(axis = 1) # min
day_stats['max'] = data.max(axis = 1) # max 
day_stats['mean'] = data.mean(axis = 1) # mean
day_stats['std'] = data.std(axis = 1) # standard deviations

day_stats.head()
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步骤11 对于每一个location，计算一月份的平均风速

注意，1961年的1月和1962年的1月应该区别对待

data['date'] = data.index

# creates a column for each value from date
data['month'] = data['date'].apply(lambda date: date.month)
data['year'] = data['date'].apply(lambda date: date.year)
data['day'] = data['date'].apply(lambda date: date.day)

# gets all value from the month 1 and assign to janyary_winds
january_winds = data.query('month == 1')

# gets the mean from january_winds, using .loc to not print the mean of month, year and day
january_winds.loc[:,'RPT':"MAL"].mean()
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RPT    14.847325
VAL    12.914560
ROS    13.299624
KIL     7.199498
SHA    11.667734
BIR     8.054839
DUB    11.819355
CLA     9.512047
MUL     9.543208
CLO    10.053566
BEL    14.550520
MAL    18.028763
dtype: float64
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步骤12 对于数据记录按照年为频率取4样

data.query('month == 1 and day == 1')
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步骤13 对于数据记录按照月为频率取样

data.query('day == 1')
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216 rows × 16 columns

练习7-可视化

探索泰坦尼克灾难数据

步骤1 导入必要的库

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

%matplotlib inline
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步骤2 从以下地址导入数据

path7 = './exercise_data/train.csv' 
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步骤3 将数据框命名为titanic

titanic = pd.read_csv(path7)
titanic.head()
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步骤4 将PassengerId设置为索引

titanic.set_index('PassengerId').head()
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步骤5 绘制一个展示男女乘客比例的扇形图

males = (titanic['Sex'] == 'male').sum()
females = (titanic['Sex'] == 'female').sum()

# put them into a list called proportions
proportions = [males, females]

# Create a pie chart
plt.pie(
    # using proportions
    proportions,
    
    # with the labels being officer names
    labels = ['Males', 'Females'],
    
    # with no shadows
    shadow = False,
    
    # with colors
    colors = ['blue','red'],
    
    # with one slide exploded out
    explode = (0.15 , 0),
    
    # with the start angle at 90%
    startangle = 90,
    
    # with the percent listed as a fraction
    autopct = '%1.1f%%'
    )

# View the plot drop above
plt.axis('equal')

# Set labels
plt.title("Sex Proportion")

# View the plot
plt.tight_layout()
plt.show()
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步骤6 绘制一个展示船票Fare, 与乘客年龄和性别的散点图

lm = sns.lmplot(x = 'Age', y = 'Fare', data = titanic, hue = 'Sex', fit_reg=False)

# set title
lm.set(title = 'Fare x Age')

# get the axes object and tweak it
axes = lm.axes
axes[0,0].set_ylim(-5,)
axes[0,0].set_xlim(-5,85)
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步骤7 有多少人生还？

titanic.Survived.sum()
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342
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步骤8 绘制一个展示船票价格的直方图

df = titanic.Fare.sort_values(ascending = False)
df

# create bins interval using numpy
binsVal = np.arange(0,600,10)
binsVal

# create the plot
plt.hist(df, bins = binsVal)

# Set the title and labels
plt.xlabel('Fare')
plt.ylabel('Frequency')
plt.title('Fare Payed Histrogram')

# show the plot
plt.show()
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练习8-创建数据框

探索Pokemon数据

步骤1 导入必要的库

import pandas as pd
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步骤2 创建一个数据字典

raw_data = {"name": ['Bulbasaur', 'Charmander','Squirtle','Caterpie'],
            "evolution": ['Ivysaur','Charmeleon','Wartortle','Metapod'],
            "type": ['grass', 'fire', 'water', 'bug'],
            "hp": [45, 39, 44, 45],
            "pokedex": ['yes', 'no','yes','no']                        
            }
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步骤3 将数据字典存为一个名叫pokemon的数据框中

pokemon = pd.DataFrame(raw_data)
pokemon.head()
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步骤4 数据框的列排序是字母顺序，请重新修改为name, type, hp, evolution, pokedex这个顺序

pokemon = pokemon[['name', 'type', 'hp', 'evolution','pokedex']]
pokemon
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步骤5 添加一个列place

pokemon['place'] = ['park','street','lake','forest']
pokemon
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步骤6 查看每个列的数据类型

pokemon.dtypes
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name         object
type         object
hp            int64
evolution    object
pokedex      object
place        object
dtype: object
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练习9-时间序列

探索Apple公司股价数据

步骤1 导入必要的库

import pandas as pd
import numpy as np

# visualization
import matplotlib.pyplot as plt

%matplotlib inline
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步骤2 数据集地址

path9 = './exercise_data/Apple_stock.csv'
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步骤3 读取数据并存为一个名叫apple的数据框

apple = pd.read_csv(path9)
apple.head()
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步骤4 查看每一列的数据类型

apple.dtypes
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Date          object
Open         float64
High         float64
Low          float64
Close        float64
Volume         int64
Adj Close    float64
dtype: object
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步骤5 将Date这个列转换为datetime类型

apple.Date = pd.to_datetime(apple.Date)
apple['Date'].head()
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0   2014-07-08
1   2014-07-07
2   2014-07-03
3   2014-07-02
4   2014-07-01
Name: Date, dtype: datetime64[ns]
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步骤6 将Date设置为索引

apple = apple.set_index('Date')
apple.head()
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步骤7 有重复的日期吗？

apple.index.is_unique
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True
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步骤8 将index设置为升序

apple.sort_index(ascending = True).head()
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步骤9 找到每个月的最后一个交易日(business day)

apple_month = apple.resample('BM').mean()
apple_month
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404 rows × 6 columns

步骤10 数据集中最早的日期和最晚的日期相差多少天？

(apple.index.max() - apple.index.min()).days
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12261
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步骤11 在数据中一共有多少个月？

apple_months = apple.resample('BM').mean()
len(apple_months.index)
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404
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步骤12 按照时间顺序可视化Adj Close值

appl_open = apple['Adj Close'].plot(title = "Apple Stock")

# changes the size of the graph
fig = appl_open.get_figure()
fig.set_size_inches(13.5, 9)
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练习10-删除数据

探索Iris纸鸢花数据

步骤1 导入必要的库

import pandas as pd
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步骤2 数据集地址

path10 ='./exercise_data/iris.csv' 
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步骤3 将数据集存成变量iris

iris = pd.read_csv(path10)
iris.head()
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步骤4 创建数据框的列名称

iris = pd.read_csv(path10,names = ['sepal_length','sepal_width', 'petal_length', 'petal_width', 'class'])
iris.head()
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步骤5 数据框中有缺失值吗？

pd.isnull(iris).sum()
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sepal_length    0
sepal_width     0
petal_length    0
petal_width     0
class           0
dtype: int64
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步骤6 将列petal_length的第10到19行设置为缺失值

iris.iloc[10:20,2:3] = np.nan
iris.head(20)
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步骤7 将缺失值全部替换为1.0

iris.petal_length.fillna(1, inplace = True)
iris
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150 rows × 5 columns

步骤8 删除列class

del iris['class']
iris.head()
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步骤9 将数据框前三行设置为缺失值

iris.iloc[0:3 ,:] = np.nan
iris.head()
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步骤10 删除有缺失值的行

iris = iris.dropna(how='any')
iris.head()
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步骤11 重新设置索引

iris = iris.reset_index(drop = True)
iris.head()
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