数据科学和人工智能技术笔记十九、数据整理（2）

十九、数据整理（2）

作者：Chris Albon

译者：飞龙

协议：CC BY-NC-SA 4.0

将变量转换为时间序列

# 导入库
import pandas as pd

# 创建索引为一组名称的数据集
raw_data = {'date': ['2014-06-01T01:21:38.004053', '2014-06-02T01:21:38.004053', '2014-06-03T01:21:38.004053'],
        'score': [25, 94, 57]}
df = pd.DataFrame(raw_data, columns = ['date', 'score'])
df

	date	score
0	2014-06-01T01:21:38.004053	25
1	2014-06-02T01:21:38.004053	94
2	2014-06-03T01:21:38.004053	57

# 转置数据集，使索引（在本例中为名称）为列
df["date"] = pd.to_datetime(df["date"])

df = df.set_index(df["date"])

df

	date	score
date
---	---	---
2014-06-01 01:21:38.004053	2014-06-01 01:21:38.004053	25
2014-06-02 01:21:38.004053	2014-06-02 01:21:38.004053	94
2014-06-03 01:21:38.004053	2014-06-03 01:21:38.004053	57

在 Pandas 数据帧中计数

# 导入库
import pandas as pd

year = pd.Series([1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882, 1883, 1884, 
                  1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893, 1894])
guardCorps = pd.Series([0,2,2,1,0,0,1,1,0,3,0,2,1,0,0,1,0,1,0,1])
corps1 = pd.Series([0,0,0,2,0,3,0,2,0,0,0,1,1,1,0,2,0,3,1,0])
corps2 = pd.Series([0,0,0,2,0,2,0,0,1,1,0,0,2,1,1,0,0,2,0,0])
corps3 = pd.Series([0,0,0,1,1,1,2,0,2,0,0,0,1,0,1,2,1,0,0,0])
corps4 = pd.Series([0,1,0,1,1,1,1,0,0,0,0,1,0,0,0,0,1,1,0,0])
corps5 = pd.Series([0,0,0,0,2,1,0,0,1,0,0,1,0,1,1,1,1,1,1,0])
corps6 = pd.Series([0,0,1,0,2,0,0,1,2,0,1,1,3,1,1,1,0,3,0,0])
corps7 = pd.Series([1,0,1,0,0,0,1,0,1,1,0,0,2,0,0,2,1,0,2,0])
corps8 = pd.Series([1,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,1,1,0,1])
corps9 = pd.Series([0,0,0,0,0,2,1,1,1,0,2,1,1,0,1,2,0,1,0,0])
corps10 = pd.Series([0,0,1,1,0,1,0,2,0,2,0,0,0,0,2,1,3,0,1,1])
corps11 = pd.Series([0,0,0,0,2,4,0,1,3,0,1,1,1,1,2,1,3,1,3,1])
corps14 = pd.Series([ 1,1,2,1,1,3,0,4,0,1,0,3,2,1,0,2,1,1,0,0])
corps15 = pd.Series([0,1,0,0,0,0,0,1,0,1,1,0,0,0,2,2,0,0,0,0])

variables = dict(guardCorps = guardCorps, corps1 = corps1, 
                 corps2 = corps2, corps3 = corps3, corps4 = corps4, 
                 corps5 = corps5, corps6 = corps6, corps7 = corps7, 
                 corps8 = corps8, corps9 = corps9, corps10 = corps10, 
                 corps11 = corps11 , corps14 = corps14, corps15 = corps15)

horsekick = pd.DataFrame(variables, columns = ['guardCorps', 
                                                    'corps1', 'corps2', 
                                                    'corps3', 'corps4', 
                                                    'corps5', 'corps6', 
                                                    'corps7', 'corps8', 
                                                    'corps9', 'corps10', 
                                                    'corps11', 'corps14', 
                                                    'corps15'])

horsekick.index = [1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882, 1883, 1884, 
                  1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893, 1894]

horsekick

	guardCorps	corps1	corps2	corps3	corps4	corps5	corps6	corps7	corps8	corps9	corps10	corps11	corps14	corps15
1875	0	0	0	0	0	0	0	1	1	0	0	0	1	0
1876	2	0	0	0	1	0	0	0	0	0	0	0	1	1
1877	2	0	0	0	0	0	1	1	0	0	1	0	2	0
1878	1	2	2	1	1	0	0	0	0	0	1	0	1	0
1879	0	0	0	1	1	2	2	0	1	0	0	2	1	0
1880	0	3	2	1	1	1	0	0	0	2	1	4	3	0
1881	1	0	0	2	1	0	0	1	0	1	0	0	0	0
1882	1	2	0	0	0	0	1	0	1	1	2	1	4	1
1883	0	0	1	2	0	1	2	1	0	1	0	3	0	0
1884	3	0	1	0	0	0	0	1	0	0	2	0	1	1
1885	0	0	0	0	0	0	1	0	0	2	0	1	0	1
1886	2	1	0	0	1	1	1	0	0	1	0	1	3	0
1887	1	1	2	1	0	0	3	2	1	1	0	1	2	0
1888	0	1	1	0	0	1	1	0	0	0	0	1	1	0
1889	0	0	1	1	0	1	1	0	0	1	2	2	0	2
1890	1	2	0	2	0	1	1	2	0	2	1	1	2	2
1891	0	0	0	1	1	1	0	1	1	0	3	3	1	0
1892	1	3	2	0	1	1	3	0	1	1	0	1	1	0
1893	0	1	0	0	0	1	0	2	0	0	1	3	0	0
1894	1	0	0	0	0	0	0	0	1	0	1	1	0	0

# 计算每个团队中每个死亡人数的次数
result = horsekick.apply(pd.value_counts).fillna(0); result

| | guardCorps | corps1 | corps2 | corps3 | corps4 | corps5 | corps6 | corps7 | corps8 | corps9 | corps10 | corps11 | corps14 | corps15 |
| 0 | 9.0 | 11.0 | 12.0 | 11.0 | 12.0 | 10.0 | 9.0 | 11.0 | 13.0 | 10.0 | 10.0 | 6 | 6 | 14.0 |
| 1 | 7.0 | 4.0 | 4.0 | 6.0 | 8.0 | 9.0 | 7.0 | 6.0 | 7.0 | 7.0 | 6.0 | 8 | 8 | 4.0 |
| 2 | 3.0 | 3.0 | 4.0 | 3.0 | 0.0 | 1.0 | 2.0 | 3.0 | 0.0 | 3.0 | 3.0 | 2 | 3 | 2.0 |
| 3 | 1.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | 0.0 | 0.0 | 0.0 | 1.0 | 3 | 2 | 0.0 |
| 4 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1 | 1 | 0.0 |

# 计算每个月死亡总数出现在 guardCorps 的次数
pd.value_counts(horsekick['guardCorps'].values, sort=False)

'''
0    9
1    7
2    3
3    1
dtype: int64 
'''

horsekick['guardCorps'].unique()

# array([0, 2, 1, 3])

在 Pandas 中创建流水线

Pandas 的流水线功能允许你将 Python 函数串联在一起，来构建数据处理流水线。

import pandas as pd

# 创建空数据帧
df = pd.DataFrame()

# Create a column
df['name'] = ['John', 'Steve', 'Sarah']
df['gender'] = ['Male', 'Male', 'Female']
df['age'] = [31, 32, 19]

# 查看数据帧
df

	name	gender	age
0	John	Male	31
1	Steve	Male	32
2	Sarah	Female	19

# 创建函数，
def mean_age_by_group(dataframe, col):
    # 它按列分组数据，并返回每组的均值
    return dataframe.groupby(col).mean()

# 创建函数，
def uppercase_column_name(dataframe):
    # 它大写所有列标题
    dataframe.columns = dataframe.columns.str.upper()
    # 并返回它
    return dataframe

# 创建流水线，它应用 mean_age_by_group 函数
(df.pipe(mean_age_by_group, col='gender')
   # 之后应用 uppercase_column_name 函数
   .pipe(uppercase_column_name)
)

	AGE
gender
Female	19.0
Male	31.5

使用`for`循环创建 Pandas 列

import pandas as pd
import numpy as np

raw_data = {'student_name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze', 'Jacon', 'Ryaner', 'Sone', 'Sloan', 'Piger', 'Riani', 'Ali'], 
        'test_score': [76, 88, 84, 67, 53, 96, 64, 91, 77, 73, 52, np.NaN]}
df = pd.DataFrame(raw_data, columns = ['student_name', 'test_score'])

# 创建列表来储存数据
grades = []

# 对于列中的每一行
for row in df['test_score']:
    # 如果大于某个值
    if row > 95:
        # 添加字母分数
        grades.append('A')
    # 或者，如果大于某个值
    elif row > 90:
        # 添加字母分数
        grades.append('A-')
    # 或者，如果大于某个值
    elif row > 85:
        # 添加字母分数
        grades.append('B')
    # 或者，如果大于某个值
    elif row > 80:
        # 添加字母分数
        grades.append('B-')
    # 或者，如果大于某个值
    elif row > 75:
        # 添加字母分数
        grades.append('C')
    # 或者，如果大于某个值
    elif row > 70:
        # 添加字母分数
        grades.append('C-')
    # 或者，如果大于某个值
    elif row > 65:
        # 添加字母分数
        grades.append('D')
    # 或者，如果大于某个值
    elif row > 60:
        # 添加字母分数
        grades.append('D-')
    # 否则
    else:
        # 添加不及格分数
        grades.append('Failed')

# 从列表创建一列
df['grades'] = grades

# 查看新数据帧
df

	student_name	test_score	grades
0	Miller	76.0	C
1	Jacobson	88.0	B
2	Ali	84.0	B-
3	Milner	67.0	D
4	Cooze	53.0	Failed
5	Jacon	96.0	A
6	Ryaner	64.0	D-
7	Sone	91.0	A-
8	Sloan	77.0	C
9	Piger	73.0	C-
10	Riani	52.0	Failed
11	Ali	NaN	Failed

创建项目计数

from collections import Counter

# 创建一个今天吃的水果的计数器
fruit_eaten = Counter(['Apple', 'Apple', 'Apple', 'Banana', 'Pear', 'Pineapple'])

# 查看计数器
fruit_eaten

# Counter({'Apple': 3, 'Banana': 1, 'Pear': 1, 'Pineapple': 1}) 

# 更新菠萝的计数（因为你只吃菠萝）
fruit_eaten.update(['Pineapple'])

# 查看计数器
fruit_eaten

# Counter({'Apple': 3, 'Banana': 1, 'Pear': 1, 'Pineapple': 2}) 

# 查看计数最大的三个项目
fruit_eaten.most_common(3)

# [('Apple', 3), ('Pineapple', 2), ('Banana', 1)]

基于条件创建一列

# 导入所需模块
import pandas as pd
import numpy as np

data = {'name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'age': [42, 52, 36, 24, 73], 
        'preTestScore': [4, 24, 31, 2, 3],
        'postTestScore': [25, 94, 57, 62, 70]}
df = pd.DataFrame(data, columns = ['name', 'age', 'preTestScore', 'postTestScore'])
df

	name	age	preTestScore	postTestScore
0	Jason	42	4	25
1	Molly	52	24	94
2	Tina	36	31	57
3	Jake	24	2	62
4	Amy	73	3	70

# 创建一个名为 df.elderly 的新列
# 如果 df.age 大于 50 则值为 yes，否则为 no
df['elderly'] = np.where(df['age']>=50, 'yes', 'no')

# 查看数据帧
df

	name	age	preTestScore	postTestScore	elderly
0	Jason	42	4	25	no
1	Molly	52	24	94	yes
2	Tina	36	31	57	no
3	Jake	24	2	62	no
4	Amy	73	3	70	yes

从词典键和值创建列表

# 创建字典
dict = {'county': ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'], 
        'year': [2012, 2012, 2013, 2014, 2014], 
        'fireReports': [4, 24, 31, 2, 3]}

# 创建键的列表
list(dict.keys())

# ['fireReports', 'year', 'county'] 

# 创建值的列表
list(dict.values())

'''
[[4, 24, 31, 2, 3],
 [2012, 2012, 2013, 2014, 2014],
 ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma']] 
'''

Pandas 中的交叉表

# 导入库
import pandas as pd

raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'], 
        'company': ['infantry', 'infantry', 'cavalry', 'cavalry', 'infantry', 'infantry', 'cavalry', 'cavalry','infantry', 'infantry', 'cavalry', 'cavalry'], 
        'experience': ['veteran', 'rookie', 'veteran', 'rookie', 'veteran', 'rookie', 'veteran', 'rookie','veteran', 'rookie', 'veteran', 'rookie'],
        'name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze', 'Jacon', 'Ryaner', 'Sone', 'Sloan', 'Piger', 'Riani', 'Ali'], 
        'preTestScore': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3],
        'postTestScore': [25, 94, 57, 62, 70, 25, 94, 57, 62, 70, 62, 70]}
df = pd.DataFrame(raw_data, columns = ['regiment', 'company', 'experience', 'name', 'preTestScore', 'postTestScore'])
df

	regiment	company	experience	name	preTestScore	postTestScore
0	Nighthawks	infantry	veteran	Miller	4	25
1	Nighthawks	infantry	rookie	Jacobson	24	94
2	Nighthawks	cavalry	veteran	Ali	31	57
3	Nighthawks	cavalry	rookie	Milner	2	62
4	Dragoons	infantry	veteran	Cooze	3	70
5	Dragoons	infantry	rookie	Jacon	4	25
6	Dragoons	cavalry	veteran	Ryaner	24	94
7	Dragoons	cavalry	rookie	Sone	31	57
8	Scouts	infantry	veteran	Sloan	2	62
9	Scouts	infantry	rookie	Piger	3	70
10	Scouts	cavalry	veteran	Riani	2	62
11	Scouts	cavalry	rookie	Ali	3	70

按公司和团队创建交叉表。按公司和团队计算观测数量。

pd.crosstab(df.regiment, df.company, margins=True)

company	cavalry	infantry	All
regiment
Dragoons	2	2	4
Nighthawks	2	2	4
Scouts	2	2	4
All	6	6	12

# 为每个团队创建公司和经验的交叉表
pd.crosstab([df.company, df.experience], df.regiment,  margins=True)

	regiment	Dragoons	Nighthawks	Scouts	All
company	experience
cavalry	rookie	1	1	1	3
	veteran	1	1	1	3
infantry	rookie	1	1	1	3
	veteran	1	1	1	3
All		4	4	4	12

删除重复

# 导入模块
import pandas as pd

raw_data = {'first_name': ['Jason', 'Jason', 'Jason','Tina', 'Jake', 'Amy'], 
        'last_name': ['Miller', 'Miller', 'Miller','Ali', 'Milner', 'Cooze'], 
        'age': [42, 42, 1111111, 36, 24, 73], 
        'preTestScore': [4, 4, 4, 31, 2, 3],
        'postTestScore': [25, 25, 25, 57, 62, 70]}
df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 'preTestScore', 'postTestScore'])
df

	first_name	last_name	age	preTestScore	postTestScore
0	Jason	Miller	42	4	25
1	Jason	Miller	42	4	25
2	Jason	Miller	1111111	4	25
3	Tina	Ali	36	31	57
4	Jake	Milner	24	2	62
5	Amy	Cooze	73	3	70

# 确定哪些观测是重复的
df.duplicated()

'''
0    False
1     True
2    False
3    False
4    False
5    False
dtype: bool 
'''

df.drop_duplicates()

	first_name	last_name	age	preTestScore	postTestScore
0	Jason	Miller	42	4	25
2	Jason	Miller	1111111	4	25
3	Tina	Ali	36	31	57
4	Jake	Milner	24	2	62
5	Amy	Cooze	73	3	70

# 删除 first_name 列中的重复项
# 但保留重复集中的最后一个观测
df.drop_duplicates(['first_name'], keep='last')

	first_name	last_name	age	preTestScore	postTestScore
2	Jason	Miller	1111111	4	25
3	Tina	Ali	36	31	57
4	Jake	Milner	24	2	62
5	Amy	Cooze	73	3	70

Pandas 数据帧的描述性统计

# 导入模块
import pandas as pd

data = {'name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'age': [42, 52, 36, 24, 73], 
        'preTestScore': [4, 24, 31, 2, 3],
        'postTestScore': [25, 94, 57, 62, 70]}
df = pd.DataFrame(data, columns = ['name', 'age', 'preTestScore', 'postTestScore'])
df

	name	age	preTestScore	postTestScore
0	Jason	42	4	25
1	Molly	52	24	94
2	Tina	36	31	57
3	Jake	24	2	62
4	Amy	73	3	70

5 rows × 4 columns

# 所有年龄之和
df['age'].sum()

# 227 

df['preTestScore'].mean()

# 12.800000000000001 

df['preTestScore'].cumsum()

'''
0     4
1    28
2    59
3    61
4    64
Name: preTestScore, dtype: int64 
'''

df['preTestScore'].describe()

'''
count     5.000000
mean     12.800000
std      13.663821
min       2.000000
25%       3.000000
50%       4.000000
75%      24.000000
max      31.000000
Name: preTestScore, dtype: float64 
'''

df['preTestScore'].count()

# 5 

df['preTestScore'].min()

# 2 

df['preTestScore'].max()

# 31 

df['preTestScore'].median()

# 4.0 

df['preTestScore'].var()

# 186.69999999999999 

df['preTestScore'].std()

# 13.663820841916802 

df['preTestScore'].skew()

# 0.74334524573267591 

df['preTestScore'].kurt()

# -2.4673543738411525 

df.corr()

	age	preTestScore	postTestScore
age	1.000000	-0.105651	0.328852
preTestScore	-0.105651	1.000000	0.378039
postTestScore	0.328852	0.378039	1.000000

3 rows × 3 columns

# 协方差矩阵
df.cov()

	age	preTestScore	postTestScore
age	340.80	-26.65	151.20
preTestScore	-26.65	186.70	128.65
postTestScore	151.20	128.65	620.30

3 rows × 3 columns

丢弃行或者列

# 导入模块
import pandas as pd

data = {'name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'year': [2012, 2012, 2013, 2014, 2014], 
        'reports': [4, 24, 31, 2, 3]}
df = pd.DataFrame(data, index = ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'])
df

	name	reports	year
Cochice	Jason	4	2012
Pima	Molly	24	2012
Santa Cruz	Tina	31	2013
Maricopa	Jake	2	2014
Yuma	Amy	3	2014

# 丢弃观测（行）
df.drop(['Cochice', 'Pima'])

	name	reports	year
Santa Cruz	Tina	31	2013
Maricopa	Jake	2	2014
Yuma	Amy	3	2014

# 丢弃变量（列）
# 注意：`axis = 1`表示我们指的是列，而不是行
df.drop('reports', axis=1)

	name	year
Cochice	Jason	2012
Pima	Molly	2012
Santa Cruz	Tina	2013
Maricopa	Jake	2014
Yuma	Amy	2014

如果它包含某个值（这里是Tina），丢弃一行。

具体来说：创建一个名为df的新数据框，名称列中的单元格的值不等于Tina。

df[df.name != 'Tina']

	name	reports	year
Cochice	Jason	4	2012
Pima	Molly	24	2012
Maricopa	Jake	2	2014
Yuma	Amy	3	2014

按照行号丢弃一行（在本例中为第 3 行）。

请注意，Pandas使用从零开始的编号，因此 0 是第一行，1 是第二行，等等。

df.drop(df.index[2])

	name	reports	year
Cochice	Jason	4	2012
Pima	Molly	24	2012
Maricopa	Jake	2	2014
Yuma	Amy	3	2014

可以扩展到范围。

df.drop(df.index[[2,3]])

	name	reports	year
Cochice	Jason	4	2012
Pima	Molly	24	2012
Yuma	Amy	3	2014

或相对于 DF 的末尾来丢弃。

df.drop(df.index[-2])

	name	reports	year
Cochice	Jason	4	2012
Pima	Molly	24	2012
Santa Cruz	Tina	31	2013
Yuma	Amy	3	2014

你也可以选择相对于起始或末尾的范围。

df[:3] # 保留前三个

	name	reports	year
Cochice	Jason	4	2012
Pima	Molly	24	2012
Santa Cruz	Tina	31	2013

df[:-3] # 丢掉后三个

	name	reports	year
Cochice	Jason	4	2012
Pima	Molly	24	2012

枚举列表

# 创建字符串列表
data = ['One','Two','Three','Four','Five']

# 对于 enumerate(data) 中的每个项目
for item in enumerate(data):
    # 打印整个枚举的元素
    print(item)
    # 只打印值（没有索引）
    print(item[1])

'''
(0, 'One')
One
(1, 'Two')
Two
(2, 'Three')
Three
(3, 'Four')
Four
(4, 'Five')
Five 
'''

在 Pandas 中将包含列表的单元扩展为自己的变量

# 导入 pandas
import pandas as pd

# 创建数据集
raw_data = {'score': [1,2,3], 
        'tags': [['apple','pear','guava'],['truck','car','plane'],['cat','dog','mouse']]}
df = pd.DataFrame(raw_data, columns = ['score', 'tags'])

# 查看数据集
df

	score	tags
0	1	[apple, pear, guava]
1	2	[truck, car, plane]
2	3	[cat, dog, mouse]

# 将 df.tags 扩展为自己的数据帧
tags = df['tags'].apply(pd.Series)

# 将每个变量重命名为标签
tags = tags.rename(columns = lambda x : 'tag_' + str(x))

# 查看 tags 数据帧
tags

	tag_0	tag_1	tag_2
0	apple	pear	guava
1	truck	car	plane
2	cat	dog	mouse

# 将 tags 数据帧添加回原始数据帧
pd.concat([df[:], tags[:]], axis=1)

	score	tags	tag_0	tag_1	tag_2
0	1	[apple, pear, guava]	apple	pear	guava
1	2	[truck, car, plane]	truck	car	plane
2	3	[cat, dog, mouse]	cat	dog	mouse

过滤 pandas 数据帧

# 导入模块
import pandas as pd

data = {'name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'year': [2012, 2012, 2013, 2014, 2014], 
        'reports': [4, 24, 31, 2, 3],
        'coverage': [25, 94, 57, 62, 70]}
df = pd.DataFrame(data, index = ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'])
df

	coverage	name	reports	year
Cochice	25	Jason	4	2012
Pima	94	Molly	24	2012
Santa Cruz	57	Tina	31	2013
Maricopa	62	Jake	2	2014
Yuma	70	Amy	3	2014

# 查看列
df['name']

'''
Cochice       Jason
Pima          Molly
Santa Cruz     Tina
Maricopa       Jake
Yuma            Amy
Name: name, dtype: object 
'''

df[['name', 'reports']]

	name	reports
Cochice	Jason	4
Pima	Molly	24
Santa Cruz	Tina	31
Maricopa	Jake	2
Yuma	Amy	3

# 查看前两行
df[:2]

	coverage	name	reports	year
Cochice	25	Jason	4	2012
Pima	94	Molly	24	2012

# 查看 Coverage 大于 50 的行
df[df['coverage'] > 50]

	coverage	name	reports	year
Pima	94	Molly	24	2012
Santa Cruz	57	Tina	31	2013
Maricopa	62	Jake	2	2014
Yuma	70	Amy	3	2014

# 查看 Coverage 大于 50 并且 Reports 小于 4 的行
df[(df['coverage']  > 50) & (df['reports'] < 4)]

	coverage	name	reports	year
Maricopa	62	Jake	2	2014
Yuma	70	Amy	3	2014

寻找数据帧的列中的最大值

# 导入模块
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

# 创建数据帧
raw_data = {'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'last_name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze'], 
        'age': [42, 52, 36, 24, 73], 
        'preTestScore': [4, 24, 31, 2, 3],
        'postTestScore': [25, 94, 57, 62, 70]}
df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 'preTestScore', 'postTestScore'])
df

	first_name	last_name	age	preTestScore	postTestScore
0	Jason	Miller	42	4	25
1	Molly	Jacobson	52	24	94
2	Tina	Ali	36	31	57
3	Jake	Milner	24	2	62
4	Amy	Cooze	73	3	70

# 获取 preTestScore 列中的最大值的索引
df['preTestScore'].idxmax()

# 2

人面猴
序言：七十年代末，一起剥皮案震惊了整个滨河市，随后出现的几起案子，更是在滨河造成了极大的恐慌，老刑警刘岩，带你破解...
沈念sama阅读 224,896评论 6赞 522
死咒
序言：滨河连续发生了三起死亡事件，死亡现场离奇诡异，居然都是意外死亡，警方通过查阅死者的电脑和手机，发现死者居然都...
沈念sama阅读 96,283评论 3赞 402
救了他两次的神仙让他今天三更去死
文/潘晓璐我一进店门，熙熙楼的掌柜王于贵愁眉苦脸地迎上来，“玉大人，你说我怎么就摊上这事。” “怎么了？”我有些...
开封第一讲书人阅读 172,085评论 0赞 367
道士缉凶录：失踪的卖姜人
文/不坏的土叔我叫张陵，是天一观的道长。经常有香客问我，道长，这世上最难降的妖魔是什么？我笑而不...
开封第一讲书人阅读 61,010评论 1赞 300
港岛之恋（遗憾婚礼）
正文为了忘掉前任，我火速办了婚礼，结果婚礼上，老公的妹妹穿的比我还像新娘。我一直安慰自己，他们只是感情好，可当我...
茶点故事阅读 70,015评论 6赞 400
恶毒庶女顶嫁案：这布局不是一般人想出来的
文/花漫我一把揭开白布。她就那样静静地躺着，像睡着了一般。火红的嫁衣衬着肌肤如雪。梳的纹丝不乱的头发上，一...
开封第一讲书人阅读 53,492评论 1赞 314
城市分裂传说
那天，我揣着相机与录音，去河边找鬼。笑死，一个胖子当着我的面吹牛，可吹牛的内容都是我干的。我是一名探鬼主播，决...
沈念sama阅读 41,858评论 3赞 428
双鸳鸯连环套：你想象不到人心有多黑
文/苍兰香墨我猛地睁开眼，长吁一口气：“原来是场噩梦啊……” “哼！你这毒妇竟也来了？” 一声冷哼从身侧响起，我...
开封第一讲书人阅读 40,829评论 0赞 279
万荣杀人案实录
序言：老挝万荣一对情侣失踪，失踪者是张志新（化名）和其女友刘颖，没想到半个月后，有当地人在树林里发现了一具尸体，经...
沈念sama阅读 47,374评论 1赞 324
护林员之死
正文独居荒郊野岭守林人离奇死亡，尸身上长有42处带血的脓包…… 初始之章·张勋以下内容为张勋视角年9月15日...
茶点故事阅读 39,409评论 3赞 346
白月光启示录
正文我和宋清朗相恋三年，在试婚纱的时候发现自己被绿了。大学时的朋友给我发了我未婚夫和他白月光在一起吃饭的照片。...
茶点故事阅读 41,527评论 1赞 355
活死人
序言：一个原本活蹦乱跳的男人离奇死亡，死状恐怖，灵堂内的尸体忽然破棺而出，到底是诈尸还是另有隐情，我是刑警宁泽，带...
沈念sama阅读 37,131评论 5赞 351
日本核电站爆炸内幕
正文年R本政府宣布，位于F岛的核电站，受9级特大地震影响，放射性物质发生泄漏。R本人自食恶果不足惜，却给世界环境...
茶点故事阅读 42,858评论 3赞 339
男人毒药：我在死后第九天来索命
文/蒙蒙一、第九天我趴在偏房一处隐蔽的房顶上张望。院中可真热闹，春花似锦、人声如沸。这庄子的主人今日做“春日...
开封第一讲书人阅读 33,296评论 0赞 25
一桩弑父案，背后竟有这般阴谋
文/苍兰香墨我抬头看了看天上的太阳。三九已至，却和暖如春，着一层夹袄步出监牢的瞬间，已是汗流浃背。一阵脚步声响...
开封第一讲书人阅读 34,434评论 1赞 276
情欲美人皮
我被黑心中介骗来泰国打工，没想到刚下飞机就差点儿被人妖公主榨干…… 1. 我叫王不留，地道东北人。一个月前我还...
沈念sama阅读 50,087评论 3赞 381
代替公主和亲
正文我出身青楼，却偏偏与公主长得像，于是被迫代替她去往敌国和亲。传闻我的和亲对象是个残疾皇子，可洞房花烛夜当晚...
茶点故事阅读 46,597评论 2赞 366

数据科学和人工智能技术笔记 十九、数据整理（2）