机器学习 - Pandas 练习, 常见功能查阅

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机器学习记录

Pandas

Pandas 官网

安装 pandas 库:

conda install -y pandas openpyxl

`pd.show_versions()`

查看版本信息

import os
import pandas as pd
import requestsPATH = r"/your_local_dir"r = requests.get('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data')with open(PATH + 'iris.data', 'w') as f:f.write(r.text)os.chdir(PATH)# 读取 csv 文件
df = pd.read_csv(PATH + 'iris.data', names=['sepal length', 'sepal width', 'petal length', 'petal width', 'class'])# 读取前几条数据
print(df.head())# 读取指定列
print(df['sepal length'])# 读取指定行和列
print(df.loc[3:5, ['sepal length', 'petal length', 'class']])# 读取指定行和列
print(df.iloc[3:5, [0, 2, 4]])# 读取列名
print(df.columns)# class 列去重后的值
print(df['class'].unique())# 统计每列的数量
print(df.count())# 根据 class 进行筛选, 序号还是原来的
print(df[df['class'] == 'Iris-versicolor'])print(df[df['class'] == 'Iris-versicolor'].count())# 根据 class 进行筛选, 重新编号, 从 0 开始
versicolor = df[df['class'] == 'Iris-versicolor'].reset_index(drop=True)
print(versicolor)# 筛选数据
print(df[(df['class'] == 'Iris-versicolor') & (df['petal width'] > 1.5)])# 各列统计信息
print(df.describe())# 指定统计的百分比
print(df.describe(percentiles=[.20, .40, .80, .90, .95]))# Pearson 相关系数
print(df.corr())print(df.corr(method="spearman"))print(df.corr(method="kendall"))

`.describe()`

对数据集进行统计描述, 包括计数, 均值, 标准差, 最小值, 最大值和四分位数等.

只对数值型和时间型数据有效, 对于文本型数据, 只会给出计数, 唯一值, 最大频率和数据类型等信息.
返回值是一个 DataFrame, 每列是一个统计指标, 每行是数据集中的一种数值型或时间型变量.

`.corr()`

用于计算 DataFrame 中列之间的相关系数(correlation coefficient)的方法. 相关系数可以衡量两个变量之间的线性关系强度, 取值范围是-1到1, 值越接近1表示两个变量越正相关, 值越接近-1表示两个变量越负相关, 值为0表示两个变量之间没有线性关系.

pandas corr 方法语法如下:

DataFrame.corr(method='pearson', min_periods=1)

其中, 参数 method 指定所使用的相关系数计算方法, 可以是 pearson(默认), spearman(Spearman’s) 或 kendall(Kendall’s). 参数 min_periods 为最小的非空值数量, 当非空值数量小于 min_periods 时, 返回 NaN.

皮尔逊积矩相关系数公式(Pearson product-moment correlation coefficient, PPMCC)

该公式计算两个变量之间的线性关系, 其值介于 -1 和 +1 之间, 表示两个变量之间的相关性强度和方向. 具体公式为:

r = (n * Σ(xy) - Σ x * Σ y) / sqrt((n * Σ(x^2) - (Σ x)^2) * (n * Σ(y^2) - (Σ y)^2))

其中, x 和 y 分别表示两个变量的值, Σ 表示求和符号, n 表示样本个数, r 表示相关系数.

Kendall

Kendall 相关系统是用于度量数据集中的相关性和相似性的数学和统计工具. 其中最常用的是 Kendall 相关系数和 Kendall tau 相关系数. 这两种相关系数都是用于比较两组顺序或排名数据的相似性的方法.

Kendall 相关系数是一种度量两组数据(X 和 Y)之间的相关性的方法. 它的范围在 -1 和 1 之间, 其中 -1 表示完全的反相关, 0 表示无关, 1 表示完全相关.
Kendall tau 相关系数是一种度量两组数据(X 和 Y)之间的相似性的方法. 它的范围在 -1 和 1 之间, 其中 -1 表示完全的相反, 0 表示无关, 1 表示完全相同.

Kendall’s tau

可以用来测试排名数据中变量之间的关系. 它的公式为:

τ = (2/ n(n-1)) Σ Σ sign(x_i - x_j) sign(y_i - y_j)

其中, n 是样本大小, x_i 和 y_i 是第 i 个观察值的排名, i ≠ j. sign()是符号函数, 如果其参数为正则返回1, 否则返回-1.

该公式计算每个组合的符号和符号积, 并将它们加起来. 如果两个变量是同向的(即它们都按相同的方向移动), 则它们的符号积将是1, 否则它将是-1. 最终的结果将在-1到1之间, 其中-1表示完全相反的排名, 而1表示完全相同的排名.

Spearman’s

一种衡量两个变量相关性的方法, 也称为 Spearman’s 秩相关系数.

在 Spearman’s 中, 每个变量的值都被转换成它们的排序值(例如, 从最小值到最大值的排名). 然后, 比较这些排名, 以确定变量之间的关系强度.

Spearman’s 秩相关系数的公式如下:

ρ = 1 - (6Σ d² / n(n²-1))

其中,

ρ 是 Spearman’s 秩相关系数
d 是排名之间的差异
n 是双方比较的对象数量

Spearman’s 秩相关系数的范围是 -1 到 1, 其值的符号表示相关性的方向.

如果 Spearman’s 秩相关系数等于 1, 则两个变量完全正相关;
如果等于 -1, 则两个变量完全负相关;
如果等于 0, 则两个变量没有相关性.

Map

适用于序列数据

修改 class 的内容, 使其值为 map 后的简称:

df['class'] = df['class'].map({'Iris-setosa': 'SET', 'Iris-virginica': 'VIR', 'Iris-versicolor': 'VER'})

选择一个不同的名称, 就可以保留原来的 class, 同时增加新的一列, 新的一列的值就是 class 的简称:

df['class_short'] = df['class'].map({'Iris-setosa': 'SET', 'Iris-virginica': 'VIR', 'Iris-versicolor': 'VER'})

通过函数来完成:

df['wide length'] = df['petal length'].map(lambda v: 2 if v >= 2 else 0)

Apply

可用于数据框和序列

通过函数来完成, 根据一列的数据来创建新的一列:

df['wide petal'] = df['petal width'].apply(lambda v: 1 if v >= 1.3 else 0)

在数据框上使用 apply (map 没有的功能):

df['petal area'] = df.apply(lambda r: r['petal length'] * r['petal width'], axis=1)

ApplyMap

对数据框里所有的数据单元执行一个函数.

常见用法: 根据一定的条件标准来转变或格式化每一个单元.

对于整个数据框的数据, 如果是 float 类型, 则取其对数, 否则使用原值:

df.applymap(lambda v: np.log(v) if isinstance(v, float) else v)

Groupby

基于某些选择的类别对数据进行分组

根据 class 进行分组, 求平均值:

df.groupby('class').mean(numeric_only=True)

numeric_only - default: False
- False: 只能使用数字
- True: 允许非数字

根据 class 进行分组, 统计信息:

df.groupby('class').describe()

根据 petal width 分类, 类别去重后得到数组.

df.groupby('petal width')['class'].unique().to_frame()

根据 class 分类, 计算 petal width 的最小值, 最大值, 以及间距

df.groupby('class')['petal width'].agg([('max', np.max), ('min', np.min),('delta', lambda x: x.max() - x.min()),
])

数据

git clone https://github.com/KeithGalli/pandas.git

练习

import pandas as pd

data_dir = "/data_dir"

df = pd.read_csv(f'{data_dir}/pandas/pokemon_data.csv')
df.shape

(800, 12)

df.head()

	#	Name	Type 1	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
0	1	Bulbasaur	Grass	Poison	45	49	49	65	65	45	1	False
1	2	Ivysaur	Grass	Poison	60	62	63	80	80	60	1	False
2	3	Venusaur	Grass	Poison	80	82	83	100	100	80	1	False
3	3	VenusaurMega Venusaur	Grass	Poison	80	100	123	122	120	80	1	False
4	4	Charmander	Fire	NaN	39	52	43	60	50	65	1	False

# 查看列的数据类型
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 795 to 799
Data columns (total 12 columns):#   Column      Non-Null Count  Dtype 
---  ------      --------------  ----- 0   #           5 non-null      int64 1   Name        5 non-null      object2   Type 1      5 non-null      object3   Type 2      5 non-null      object4   HP          5 non-null      int64 5   Attack      5 non-null      int64 6   Defense     5 non-null      int64 7   Sp. Atk     5 non-null      int64 8   Sp. Def     5 non-null      int64 9   Speed       5 non-null      int64 10  Generation  5 non-null      int64 11  Legendary   5 non-null      bool  
dtypes: bool(1), int64(8), object(3)
memory usage: 577.0+ bytes

df = pd.read_excel(f'{data_dir}/pandas/pokemon_data.xlsx')
df.shape

(800, 12)

df.head()

	#	Name	Type 1	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
0	1	Bulbasaur	Grass	Poison	45	49	49	65	65	45	1	False
1	2	Ivysaur	Grass	Poison	60	62	63	80	80	60	1	False
2	3	Venusaur	Grass	Poison	80	82	83	100	100	80	1	False
3	3	VenusaurMega Venusaur	Grass	Poison	80	100	123	122	120	80	1	False
4	4	Charmander	Fire	NaN	39	52	43	60	50	65	1	False

def read():global dfdf = pd.read_csv(f'{data_dir}/pandas/pokemon_data.csv')
read()
df.columns

Index(['#', 'Name', 'Type 1', 'Type 2', 'HP', 'Attack', 'Defense', 'Sp. Atk','Sp. Def', 'Speed', 'Generation', 'Legendary'],dtype='object')

df.Name

0                  Bulbasaur
1                    Ivysaur
2                   Venusaur
3      VenusaurMega Venusaur
4                 Charmander...          
795                  Diancie
796      DiancieMega Diancie
797      HoopaHoopa Confined
798       HoopaHoopa Unbound
799                Volcanion
Name: Name, Length: 800, dtype: object

df['Name']

0                  Bulbasaur
1                    Ivysaur
2                   Venusaur
3      VenusaurMega Venusaur
4                 Charmander...          
795                  Diancie
796      DiancieMega Diancie
797      HoopaHoopa Confined
798       HoopaHoopa Unbound
799                Volcanion
Name: Name, Length: 800, dtype: object

df[['Name', 'HP', 'Speed']]

	Name	HP	Speed
0	Bulbasaur	45	45
1	Ivysaur	60	60
2	Venusaur	80	80
3	VenusaurMega Venusaur	80	80
4	Charmander	39	65
...	...	...	...
795	Diancie	50	50
796	DiancieMega Diancie	50	110
797	HoopaHoopa Confined	80	70
798	HoopaHoopa Unbound	80	80
799	Volcanion	80	70

800 rows × 3 columns

# 第 [n] 行的数据
df.iloc[1]

#                   2
Name          Ivysaur
Type 1          Grass
Type 2         Poison
HP                 60
Attack             62
Defense            63
Sp. Atk            80
Sp. Def            80
Speed              60
Generation          1
Legendary       False
Name: 1, dtype: object

# 第 [n, m) 行的数据
df.iloc[0:4]

	#	Name	Type 1	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
0	1	Bulbasaur	Grass	Poison	45	49	49	65	65	45	1	False
1	2	Ivysaur	Grass	Poison	60	62	63	80	80	60	1	False
2	3	Venusaur	Grass	Poison	80	82	83	100	100	80	1	False
3	3	VenusaurMega Venusaur	Grass	Poison	80	100	123	122	120	80	1	False

# 第 [n] 行, [m] 列的数据
df.iloc[2, 2]

'Grass'

# 行遍历
# for index, row in df.iterrows():
# #     print(index, row)
#     print(index, row['Name'])

0 Bulbasaur
1 Ivysaur
2 Venusaur
3 VenusaurMega Venusaur
4 Charmander
5 Charmeleon
...
796 DiancieMega Diancie
797 HoopaHoopa Confined
798 HoopaHoopa Unbound
799 Volcanion

# 根据字段过滤数据
df.loc[df['Type 1'] == "Grass"]

	#	Name	Type 1	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
0	1	Bulbasaur	Grass	Poison	45	49	49	65	65	45	1	False
1	2	Ivysaur	Grass	Poison	60	62	63	80	80	60	1	False
2	3	Venusaur	Grass	Poison	80	82	83	100	100	80	1	False
3	3	VenusaurMega Venusaur	Grass	Poison	80	100	123	122	120	80	1	False
48	43	Oddish	Grass	Poison	45	50	55	75	65	30	1	False
...	...	...	...	...	...	...	...	...	...	...	...	...
718	650	Chespin	Grass	NaN	56	61	65	48	45	38	6	False
719	651	Quilladin	Grass	NaN	61	78	95	56	58	57	6	False
720	652	Chesnaught	Grass	Fighting	88	107	122	74	75	64	6	False
740	672	Skiddo	Grass	NaN	66	65	48	62	57	52	6	False
741	673	Gogoat	Grass	NaN	123	100	62	97	81	68	6	False

70 rows × 12 columns

# 统计, 只对"数值"类型统计
# count：非空值的数量。
# mean：平均值。
# std：标准差。
# min：最小值。
# 25%：下四分位数。
# 50%：中位数（下四分位数和上四分位数的平均值）。
# 75%：上四分位数。
# max：最大值。
df.describe()

	#	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation
count	800.000000	800.000000	800.000000	800.000000	800.000000	800.000000	800.000000	800.00000
mean	362.813750	69.258750	79.001250	73.842500	72.820000	71.902500	68.277500	3.32375
std	208.343798	25.534669	32.457366	31.183501	32.722294	27.828916	29.060474	1.66129
min	1.000000	1.000000	5.000000	5.000000	10.000000	20.000000	5.000000	1.00000
25%	184.750000	50.000000	55.000000	50.000000	49.750000	50.000000	45.000000	2.00000
50%	364.500000	65.000000	75.000000	70.000000	65.000000	70.000000	65.000000	3.00000
75%	539.250000	80.000000	100.000000	90.000000	95.000000	90.000000	90.000000	5.00000
max	721.000000	255.000000	190.000000	230.000000	194.000000	230.000000	180.000000	6.00000

# 排序
df.sort_values('Name')

	#	Name	Type 1	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
510	460	Abomasnow	Grass	Ice	90	92	75	92	85	60	4	False
511	460	AbomasnowMega Abomasnow	Grass	Ice	90	132	105	132	105	30	4	False
68	63	Abra	Psychic	NaN	25	20	15	105	55	90	1	False
392	359	Absol	Dark	NaN	65	130	60	75	60	75	3	False
393	359	AbsolMega Absol	Dark	NaN	65	150	60	115	60	115	3	False
...	...	...	...	...	...	...	...	...	...	...	...	...
632	571	Zoroark	Dark	NaN	60	105	60	120	60	105	5	False
631	570	Zorua	Dark	NaN	40	65	40	80	40	65	5	False
46	41	Zubat	Poison	Flying	40	45	35	30	40	55	1	False
695	634	Zweilous	Dark	Dragon	72	85	70	65	70	58	5	False
794	718	Zygarde50% Forme	Dragon	Ground	108	100	121	81	95	95	6	True

800 rows × 12 columns

# 排序: 倒序
df.sort_values('Name', ascending=False)

	#	Name	Type 1	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
794	718	Zygarde50% Forme	Dragon	Ground	108	100	121	81	95	95	6	True
695	634	Zweilous	Dark	Dragon	72	85	70	65	70	58	5	False
46	41	Zubat	Poison	Flying	40	45	35	30	40	55	1	False
631	570	Zorua	Dark	NaN	40	65	40	80	40	65	5	False
632	571	Zoroark	Dark	NaN	60	105	60	120	60	105	5	False
...	...	...	...	...	...	...	...	...	...	...	...	...
393	359	AbsolMega Absol	Dark	NaN	65	150	60	115	60	115	3	False
392	359	Absol	Dark	NaN	65	130	60	75	60	75	3	False
68	63	Abra	Psychic	NaN	25	20	15	105	55	90	1	False
511	460	AbomasnowMega Abomasnow	Grass	Ice	90	132	105	132	105	30	4	False
510	460	Abomasnow	Grass	Ice	90	92	75	92	85	60	4	False

800 rows × 12 columns

# 排序: 多字段排序
df.sort_values(['Type 1', 'HP'])

	#	Name	Type 1	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
316	292	Shedinja	Bug	Ghost	1	90	45	30	30	40	3	False
230	213	Shuckle	Bug	Rock	20	10	230	10	230	5	2	False
462	415	Combee	Bug	Flying	30	30	42	30	42	70	4	False
603	543	Venipede	Bug	Poison	30	45	59	30	39	57	5	False
314	290	Nincada	Bug	Ground	31	45	90	30	30	40	3	False
...	...	...	...	...	...	...	...	...	...	...	...	...
142	131	Lapras	Water	Ice	130	85	80	85	95	60	1	False
145	134	Vaporeon	Water	NaN	130	65	60	110	95	65	1	False
350	320	Wailmer	Water	NaN	130	70	35	70	35	60	3	False
655	594	Alomomola	Water	NaN	165	75	80	40	45	65	5	False
351	321	Wailord	Water	NaN	170	90	45	90	45	60	3	False

800 rows × 12 columns

# 排序: 多字段排序, 指定每个字段的排序顺序
df.sort_values(['Type 1', 'HP'], ascending=[0, 1])

	#	Name	Type 1	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
139	129	Magikarp	Water	NaN	20	10	55	15	20	80	1	False
381	349	Feebas	Water	NaN	20	15	20	10	55	80	3	False
97	90	Shellder	Water	NaN	30	65	100	45	25	40	1	False
106	98	Krabby	Water	NaN	30	105	90	25	25	50	1	False
125	116	Horsea	Water	NaN	30	40	70	70	25	60	1	False
...	...	...	...	...	...	...	...	...	...	...	...	...
232	214	HeracrossMega Heracross	Bug	Fighting	80	185	115	40	105	75	2	False
678	617	Accelgor	Bug	NaN	80	70	40	100	60	145	5	False
734	666	Vivillon	Bug	Flying	80	52	50	90	50	89	6	False
698	637	Volcarona	Bug	Fire	85	60	65	135	105	100	5	False
520	469	Yanmega	Bug	Flying	86	76	86	116	56	95	4	False

800 rows × 12 columns

df['Total'] = df['HP'] + df['Attack'] + df['Defense'] + df['Sp. Atk'] + df['Sp. Def'] + df['Speed']
df

	#	Name	Type 1	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary	Total
0	1	Bulbasaur	Grass	Poison	45	49	49	65	65	45	1	False	318
1	2	Ivysaur	Grass	Poison	60	62	63	80	80	60	1	False	405
2	3	Venusaur	Grass	Poison	80	82	83	100	100	80	1	False	525
3	3	VenusaurMega Venusaur	Grass	Poison	80	100	123	122	120	80	1	False	625
4	4	Charmander	Fire	NaN	39	52	43	60	50	65	1	False	309
...	...	...	...	...	...	...	...	...	...	...	...	...	...
795	719	Diancie	Rock	Fairy	50	100	150	100	150	50	6	True	600
796	719	DiancieMega Diancie	Rock	Fairy	50	160	110	160	110	110	6	True	700
797	720	HoopaHoopa Confined	Psychic	Ghost	80	110	60	150	130	70	6	True	600
798	720	HoopaHoopa Unbound	Psychic	Dark	80	160	60	170	130	80	6	True	680
799	721	Volcanion	Fire	Water	80	110	120	130	90	70	6	True	600

800 rows × 13 columns

df['Total'] = df['HP'] + df['Attack'] + df['Defense'] + df['Sp. Atk'] + df['Sp. Def'] + df['Speed']
# df.drop 不会修改 df, 需要赋值. 如果删除的 列 不存在, 则会报错
df = df.drop(columns=['Total'])df.head()

	#	Name	Type 1	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
0	1	Bulbasaur	Grass	Poison	45	49	49	65	65	45	1	False
1	2	Ivysaur	Grass	Poison	60	62	63	80	80	60	1	False
2	3	Venusaur	Grass	Poison	80	82	83	100	100	80	1	False
3	3	VenusaurMega Venusaur	Grass	Poison	80	100	123	122	120	80	1	False
4	4	Charmander	Fire	NaN	39	52	43	60	50	65	1	False

# 当 axis=0 时，对每一列进行求和，返回一行结果。
# 当 axis=1 时，对每一行进行求和，返回一列结果。
df['Total'] = df.iloc[:, 4:10].sum(axis=1)df.head()

	#	Name	Type 1	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary	Total
0	1	Bulbasaur	Grass	Poison	45	49	49	65	65	45	1	False	318
1	2	Ivysaur	Grass	Poison	60	62	63	80	80	60	1	False	405
2	3	Venusaur	Grass	Poison	80	82	83	100	100	80	1	False	525
3	3	VenusaurMega Venusaur	Grass	Poison	80	100	123	122	120	80	1	False	625
4	4	Charmander	Fire	NaN	39	52	43	60	50	65	1	False	309

read()df['Total'] = df.iloc[:, 4:10].sum(axis=1)# 输出指定列
df = df[['Total', 'HP', 'Defense']]df.head(5)

	Total	HP	Defense
0	318	45	49
1	405	60	63
2	525	80	83
3	625	80	123
4	309	39	43

read()df['Total'] = df.iloc[:, 4:10].sum(axis=1)cols = list(df.columns)
print(cols)# 输出指定列
df = df[cols[0:4] + [cols[-1]] + cols[4:-1]]df.head(5)

['#', 'Name', 'Type 1', 'Type 2', 'HP', 'Attack', 'Defense', 'Sp. Atk', 'Sp. Def', 'Speed', 'Generation', 'Legendary', 'Total']

	#	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
0	1	Bulbasaur	Grass	Poison	318	45	49	49	65	65	45	1	False
1	2	Ivysaur	Grass	Poison	405	60	62	63	80	80	60	1	False
2	3	Venusaur	Grass	Poison	525	80	82	83	100	100	80	1	False
3	3	VenusaurMega Venusaur	Grass	Poison	625	80	100	123	122	120	80	1	False
4	4	Charmander	Fire	NaN	309	39	52	43	60	50	65	1	False

# 写文件
# df.to_csv('/tmp/modified.csv', index=False)
# df.to_excel('/tmp/modified.xlsx', index=False)

# 多个字段过滤
df.loc[(df['Type 1'] == 'Grass') & (df['Type 2'] == 'Poison')]

	#	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
0	1	Bulbasaur	Grass	Poison	318	45	49	49	65	65	45	1	False
1	2	Ivysaur	Grass	Poison	405	60	62	63	80	80	60	1	False
2	3	Venusaur	Grass	Poison	525	80	82	83	100	100	80	1	False
3	3	VenusaurMega Venusaur	Grass	Poison	625	80	100	123	122	120	80	1	False
48	43	Oddish	Grass	Poison	320	45	50	55	75	65	30	1	False
49	44	Gloom	Grass	Poison	395	60	65	70	85	75	40	1	False
50	45	Vileplume	Grass	Poison	490	75	80	85	110	90	50	1	False
75	69	Bellsprout	Grass	Poison	300	50	75	35	70	30	40	1	False
76	70	Weepinbell	Grass	Poison	390	65	90	50	85	45	55	1	False
77	71	Victreebel	Grass	Poison	490	80	105	65	100	70	70	1	False
344	315	Roselia	Grass	Poison	400	50	60	45	100	80	65	3	False
451	406	Budew	Grass	Poison	280	40	30	35	50	70	55	4	False
452	407	Roserade	Grass	Poison	515	60	70	65	125	105	90	4	False
651	590	Foongus	Grass	Poison	294	69	55	45	55	55	15	5	False
652	591	Amoonguss	Grass	Poison	464	114	85	70	85	80	30	5	False

# 多个字段过滤
df.loc[(df['Type 1'] == 'Grass') | (df['Type 2'] == 'Poison')]

	#	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
0	1	Bulbasaur	Grass	Poison	318	45	49	49	65	65	45	1	False
1	2	Ivysaur	Grass	Poison	405	60	62	63	80	80	60	1	False
2	3	Venusaur	Grass	Poison	525	80	82	83	100	100	80	1	False
3	3	VenusaurMega Venusaur	Grass	Poison	625	80	100	123	122	120	80	1	False
16	13	Weedle	Bug	Poison	195	40	35	30	20	20	50	1	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...
718	650	Chespin	Grass	NaN	313	56	61	65	48	45	38	6	False
719	651	Quilladin	Grass	NaN	405	61	78	95	56	58	57	6	False
720	652	Chesnaught	Grass	Fighting	530	88	107	122	74	75	64	6	False
740	672	Skiddo	Grass	NaN	350	66	65	48	62	57	52	6	False
741	673	Gogoat	Grass	NaN	531	123	100	62	97	81	68	6	False

89 rows × 13 columns

new_df = df.loc[(df['Type 1'] == 'Grass') & (df['Type 2'] == 'Poison') & (df['HP'] > 70)]# new_df.to_csv('/tmp/filtered.csv', index=False)# "index" 是 df 的 index. 会重新为 new_df 生成index
new_df = new_df.reset_index()new_df

	index	#	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
0	2	3	Venusaur	Grass	Poison	525	80	82	83	100	100	80	1	False
1	3	3	VenusaurMega Venusaur	Grass	Poison	625	80	100	123	122	120	80	1	False
2	50	45	Vileplume	Grass	Poison	490	75	80	85	110	90	50	1	False
3	77	71	Victreebel	Grass	Poison	490	80	105	65	100	70	70	1	False
4	652	591	Amoonguss	Grass	Poison	464	114	85	70	85	80	30	5	False

new_df = df.loc[(df['Type 1'] == 'Grass') & (df['Type 2'] == 'Poison') & (df['HP'] > 70)]# 会重新为 new_df 生成index, 删除 df 保留下来的 index 列
new_df = new_df.reset_index(drop=True)new_df

	#	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
0	3	Venusaur	Grass	Poison	525	80	82	83	100	100	80	1	False
1	3	VenusaurMega Venusaur	Grass	Poison	625	80	100	123	122	120	80	1	False
2	45	Vileplume	Grass	Poison	490	75	80	85	110	90	50	1	False
3	71	Victreebel	Grass	Poison	490	80	105	65	100	70	70	1	False
4	591	Amoonguss	Grass	Poison	464	114	85	70	85	80	30	5	False

df.loc[df['Name'].str.contains('Mega')]

	#	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
3	3	VenusaurMega Venusaur	Grass	Poison	625	80	100	123	122	120	80	1	False
7	6	CharizardMega Charizard X	Fire	Dragon	634	78	130	111	130	85	100	1	False
8	6	CharizardMega Charizard Y	Fire	Flying	634	78	104	78	159	115	100	1	False
12	9	BlastoiseMega Blastoise	Water	NaN	630	79	103	120	135	115	78	1	False
19	15	BeedrillMega Beedrill	Bug	Poison	495	65	150	40	15	80	145	1	False
23	18	PidgeotMega Pidgeot	Normal	Flying	579	83	80	80	135	80	121	1	False
71	65	AlakazamMega Alakazam	Psychic	NaN	590	55	50	65	175	95	150	1	False
87	80	SlowbroMega Slowbro	Water	Psychic	590	95	75	180	130	80	30	1	False
102	94	GengarMega Gengar	Ghost	Poison	600	60	65	80	170	95	130	1	False
124	115	KangaskhanMega Kangaskhan	Normal	NaN	590	105	125	100	60	100	100	1	False
137	127	PinsirMega Pinsir	Bug	Flying	600	65	155	120	65	90	105	1	False
141	130	GyaradosMega Gyarados	Water	Dark	640	95	155	109	70	130	81	1	False
154	142	AerodactylMega Aerodactyl	Rock	Flying	615	80	135	85	70	95	150	1	False
163	150	MewtwoMega Mewtwo X	Psychic	Fighting	780	106	190	100	154	100	130	1	True
164	150	MewtwoMega Mewtwo Y	Psychic	NaN	780	106	150	70	194	120	140	1	True
168	154	Meganium	Grass	NaN	525	80	82	100	83	100	80	2	False
196	181	AmpharosMega Ampharos	Electric	Dragon	610	90	95	105	165	110	45	2	False
224	208	SteelixMega Steelix	Steel	Ground	610	75	125	230	55	95	30	2	False
229	212	ScizorMega Scizor	Bug	Steel	600	70	150	140	65	100	75	2	False
232	214	HeracrossMega Heracross	Bug	Fighting	600	80	185	115	40	105	75	2	False
248	229	HoundoomMega Houndoom	Dark	Fire	600	75	90	90	140	90	115	2	False
268	248	TyranitarMega Tyranitar	Rock	Dark	700	100	164	150	95	120	71	2	False
275	254	SceptileMega Sceptile	Grass	Dragon	630	70	110	75	145	85	145	3	False
279	257	BlazikenMega Blaziken	Fire	Fighting	630	80	160	80	130	80	100	3	False
283	260	SwampertMega Swampert	Water	Ground	635	100	150	110	95	110	70	3	False
306	282	GardevoirMega Gardevoir	Psychic	Fairy	618	68	85	65	165	135	100	3	False
327	302	SableyeMega Sableye	Dark	Ghost	480	50	85	125	85	115	20	3	False
329	303	MawileMega Mawile	Steel	Fairy	480	50	105	125	55	95	50	3	False
333	306	AggronMega Aggron	Steel	NaN	630	70	140	230	60	80	50	3	False
336	308	MedichamMega Medicham	Fighting	Psychic	510	60	100	85	80	85	100	3	False
339	310	ManectricMega Manectric	Electric	NaN	575	70	75	80	135	80	135	3	False
349	319	SharpedoMega Sharpedo	Water	Dark	560	70	140	70	110	65	105	3	False
354	323	CameruptMega Camerupt	Fire	Ground	560	70	120	100	145	105	20	3	False
366	334	AltariaMega Altaria	Dragon	Fairy	590	75	110	110	110	105	80	3	False
387	354	BanetteMega Banette	Ghost	NaN	555	64	165	75	93	83	75	3	False
393	359	AbsolMega Absol	Dark	NaN	565	65	150	60	115	60	115	3	False
397	362	GlalieMega Glalie	Ice	NaN	580	80	120	80	120	80	100	3	False
409	373	SalamenceMega Salamence	Dragon	Flying	700	95	145	130	120	90	120	3	False
413	376	MetagrossMega Metagross	Steel	Psychic	700	80	145	150	105	110	110	3	False
418	380	LatiasMega Latias	Dragon	Psychic	700	80	100	120	140	150	110	3	True
420	381	LatiosMega Latios	Dragon	Psychic	700	80	130	100	160	120	110	3	True
426	384	RayquazaMega Rayquaza	Dragon	Flying	780	105	180	100	180	100	115	3	True
476	428	LopunnyMega Lopunny	Normal	Fighting	580	65	136	94	54	96	135	4	False
494	445	GarchompMega Garchomp	Dragon	Ground	700	108	170	115	120	95	92	4	False
498	448	LucarioMega Lucario	Fighting	Steel	625	70	145	88	140	70	112	4	False
511	460	AbomasnowMega Abomasnow	Grass	Ice	594	90	132	105	132	105	30	4	False
527	475	GalladeMega Gallade	Psychic	Fighting	618	68	165	95	65	115	110	4	False
591	531	AudinoMega Audino	Normal	Fairy	545	103	60	126	80	126	50	5	False
796	719	DiancieMega Diancie	Rock	Fairy	700	50	160	110	160	110	110	6	True

import re
df.loc[df['Type 1'].str.contains('Fire|grass', regex=True, flags=re.I)]

	#	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
0	1	Bulbasaur	Grass	Poison	318	45	49	49	65	65	45	1	False
1	2	Ivysaur	Grass	Poison	405	60	62	63	80	80	60	1	False
2	3	Venusaur	Grass	Poison	525	80	82	83	100	100	80	1	False
3	3	VenusaurMega Venusaur	Grass	Poison	625	80	100	123	122	120	80	1	False
4	4	Charmander	Fire	NaN	309	39	52	43	60	50	65	1	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...
735	667	Litleo	Fire	Normal	369	62	50	58	73	54	72	6	False
736	668	Pyroar	Fire	Normal	507	86	68	72	109	66	106	6	False
740	672	Skiddo	Grass	NaN	350	66	65	48	62	57	52	6	False
741	673	Gogoat	Grass	NaN	531	123	100	62	97	81	68	6	False
799	721	Volcanion	Fire	Water	600	80	110	120	130	90	70	6	True

122 rows × 13 columns

import re
df.loc[df['Name'].str.contains('^pi[a-z]', regex=True, flags=re.I)]

	#	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
20	16	Pidgey	Normal	Flying	251	40	45	40	35	35	56	1	False
21	17	Pidgeotto	Normal	Flying	349	63	60	55	50	50	71	1	False
22	18	Pidgeot	Normal	Flying	479	83	80	75	70	70	101	1	False
23	18	PidgeotMega Pidgeot	Normal	Flying	579	83	80	80	135	80	121	1	False
30	25	Pikachu	Electric	NaN	320	35	55	40	50	50	90	1	False
136	127	Pinsir	Bug	NaN	500	65	125	100	55	70	85	1	False
137	127	PinsirMega Pinsir	Bug	Flying	600	65	155	120	65	90	105	1	False
186	172	Pichu	Electric	NaN	205	20	40	15	35	35	60	2	False
219	204	Pineco	Bug	NaN	290	50	65	90	35	35	15	2	False
239	221	Piloswine	Ice	Ground	450	100	100	80	60	60	50	2	False
438	393	Piplup	Water	NaN	314	53	51	53	61	56	40	4	False
558	499	Pignite	Fire	Fighting	418	90	93	55	70	55	55	5	False
578	519	Pidove	Normal	Flying	264	50	55	50	36	30	43	5	False

read()
# 第一个参数满足条件的, 设置第二个参数的列为后面的值
df.loc[df['Type 1'] == 'Grass', 'Type 1'] = 'Flamer'df

	#	Name	Type 1	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
0	1	Bulbasaur	Flamer	Poison	45	49	49	65	65	45	1	False
1	2	Ivysaur	Flamer	Poison	60	62	63	80	80	60	1	False
2	3	Venusaur	Flamer	Poison	80	82	83	100	100	80	1	False
3	3	VenusaurMega Venusaur	Flamer	Poison	80	100	123	122	120	80	1	False
4	4	Charmander	Fire	NaN	39	52	43	60	50	65	1	False
...	...	...	...	...	...	...	...	...	...	...	...	...
795	719	Diancie	Rock	Fairy	50	100	150	100	150	50	6	True
796	719	DiancieMega Diancie	Rock	Fairy	50	160	110	160	110	110	6	True
797	720	HoopaHoopa Confined	Psychic	Ghost	80	110	60	150	130	70	6	True
798	720	HoopaHoopa Unbound	Psychic	Dark	80	160	60	170	130	80	6	True
799	721	Volcanion	Fire	Water	80	110	120	130	90	70	6	True

800 rows × 12 columns

read()
# 第一个参数满足条件的, 设置第二个参数的列为后面的值
df.loc[df['Type 1'] == 'Grass', ['Type 1', 'Lendary']] = 'TEST VALUE'df

	#	Name	Type 1	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary	Lendary
0	1	Bulbasaur	TEST VALUE	Poison	45	49	49	65	65	45	1	False	TEST VALUE
1	2	Ivysaur	TEST VALUE	Poison	60	62	63	80	80	60	1	False	TEST VALUE
2	3	Venusaur	TEST VALUE	Poison	80	82	83	100	100	80	1	False	TEST VALUE
3	3	VenusaurMega Venusaur	TEST VALUE	Poison	80	100	123	122	120	80	1	False	TEST VALUE
4	4	Charmander	Fire	NaN	39	52	43	60	50	65	1	False	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...
795	719	Diancie	Rock	Fairy	50	100	150	100	150	50	6	True	NaN
796	719	DiancieMega Diancie	Rock	Fairy	50	160	110	160	110	110	6	True	NaN
797	720	HoopaHoopa Confined	Psychic	Ghost	80	110	60	150	130	70	6	True	NaN
798	720	HoopaHoopa Unbound	Psychic	Dark	80	160	60	170	130	80	6	True	NaN
799	721	Volcanion	Fire	Water	80	110	120	130	90	70	6	True	NaN

800 rows × 13 columns

read()
# 第一个参数满足条件的, 设置第二个参数的列为后面的值
df.loc[df['Type 1'] == 'Grass', ['Type 1', 'Lendary']] = ['TEST 1', 'TEST 2']df

	#	Name	Type 1	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary	Lendary
0	1	Bulbasaur	TEST 1	Poison	45	49	49	65	65	45	1	False	TEST 2
1	2	Ivysaur	TEST 1	Poison	60	62	63	80	80	60	1	False	TEST 2
2	3	Venusaur	TEST 1	Poison	80	82	83	100	100	80	1	False	TEST 2
3	3	VenusaurMega Venusaur	TEST 1	Poison	80	100	123	122	120	80	1	False	TEST 2
4	4	Charmander	Fire	NaN	39	52	43	60	50	65	1	False	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...
795	719	Diancie	Rock	Fairy	50	100	150	100	150	50	6	True	NaN
796	719	DiancieMega Diancie	Rock	Fairy	50	160	110	160	110	110	6	True	NaN
797	720	HoopaHoopa Confined	Psychic	Ghost	80	110	60	150	130	70	6	True	NaN
798	720	HoopaHoopa Unbound	Psychic	Dark	80	160	60	170	130	80	6	True	NaN
799	721	Volcanion	Fire	Water	80	110	120	130	90	70	6	True	NaN

800 rows × 13 columns

read()# 分组 求平均数
df.groupby(['Type 1']).mean(numeric_only=True).sort_values('Defense', ascending=False)

	#	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
Type 1
Steel	442.851852	65.222222	92.703704	126.370370	67.518519	80.629630	55.259259	3.851852	0.148148
Rock	392.727273	65.363636	92.863636	100.795455	63.340909	75.477273	55.909091	3.454545	0.090909
Dragon	474.375000	83.312500	112.125000	86.375000	96.843750	88.843750	83.031250	3.875000	0.375000
Ground	356.281250	73.781250	95.750000	84.843750	56.468750	62.750000	63.906250	3.156250	0.125000
Ghost	486.500000	64.437500	73.781250	81.187500	79.343750	76.468750	64.343750	4.187500	0.062500
Water	303.089286	72.062500	74.151786	72.946429	74.812500	70.517857	65.964286	2.857143	0.035714
Ice	423.541667	72.000000	72.750000	71.416667	77.541667	76.291667	63.458333	3.541667	0.083333
Grass	344.871429	67.271429	73.214286	70.800000	77.500000	70.428571	61.928571	3.357143	0.042857
Bug	334.492754	56.884058	70.971014	70.724638	53.869565	64.797101	61.681159	3.217391	0.000000
Dark	461.354839	66.806452	88.387097	70.225806	74.645161	69.516129	76.161290	4.032258	0.064516
Poison	251.785714	67.250000	74.678571	68.821429	60.428571	64.392857	63.571429	2.535714	0.000000
Fire	327.403846	69.903846	84.769231	67.769231	88.980769	72.211538	74.442308	3.211538	0.096154
Psychic	380.807018	70.631579	71.456140	67.684211	98.403509	86.280702	81.491228	3.385965	0.245614
Electric	363.500000	59.795455	69.090909	66.295455	90.022727	73.704545	84.500000	3.272727	0.090909
Flying	677.750000	70.750000	78.750000	66.250000	94.250000	72.500000	102.500000	5.500000	0.500000
Fighting	363.851852	69.851852	96.777778	65.925926	53.111111	64.703704	66.074074	3.370370	0.000000
Fairy	449.529412	74.117647	61.529412	65.705882	78.529412	84.705882	48.588235	4.117647	0.058824
Normal	319.173469	77.275510	73.469388	59.846939	55.816327	63.724490	71.551020	3.051020	0.020408

read()# 分组 求和
df.groupby(['Type 1']).sum(numeric_only=True).sort_values('HP', ascending=False)

	#	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
Type 1
Water	33946	8071	8305	8170	8379	7898	7388	320	4
Normal	31279	7573	7200	5865	5470	6245	7012	299	2
Grass	24141	4709	5125	4956	5425	4930	4335	235	3
Psychic	21706	4026	4073	3858	5609	4918	4645	193	14
Bug	23080	3925	4897	4880	3717	4471	4256	222	0
Fire	17025	3635	4408	3524	4627	3755	3871	167	5
Rock	17280	2876	4086	4435	2787	3321	2460	152	4
Dragon	15180	2666	3588	2764	3099	2843	2657	124	12
Electric	15994	2631	3040	2917	3961	3243	3718	144	4
Ground	11401	2361	3064	2715	1807	2008	2045	101	4
Dark	14302	2071	2740	2177	2314	2155	2361	125	2
Ghost	15568	2062	2361	2598	2539	2447	2059	134	2
Fighting	9824	1886	2613	1780	1434	1747	1784	91	0
Poison	7050	1883	2091	1927	1692	1803	1780	71	0
Steel	11957	1761	2503	3412	1823	2177	1492	104	4
Ice	10165	1728	1746	1714	1861	1831	1523	85	2
Fairy	7642	1260	1046	1117	1335	1440	826	70	1
Flying	2711	283	315	265	377	290	410	22	2

read()# 分组 求数量
df.groupby(['Type 1']).count().sort_values('HP', ascending=False)

	#	Name	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
Type 1
Water	112	112	53	112	112	112	112	112	112	112	112
Normal	98	98	37	98	98	98	98	98	98	98	98
Grass	70	70	37	70	70	70	70	70	70	70	70
Bug	69	69	52	69	69	69	69	69	69	69	69
Psychic	57	57	19	57	57	57	57	57	57	57	57
Fire	52	52	24	52	52	52	52	52	52	52	52
Electric	44	44	17	44	44	44	44	44	44	44	44
Rock	44	44	35	44	44	44	44	44	44	44	44
Ghost	32	32	22	32	32	32	32	32	32	32	32
Ground	32	32	19	32	32	32	32	32	32	32	32
Dragon	32	32	21	32	32	32	32	32	32	32	32
Dark	31	31	21	31	31	31	31	31	31	31	31
Poison	28	28	13	28	28	28	28	28	28	28	28
Fighting	27	27	7	27	27	27	27	27	27	27	27
Steel	27	27	22	27	27	27	27	27	27	27	27
Ice	24	24	11	24	24	24	24	24	24	24	24
Fairy	17	17	2	17	17	17	17	17	17	17	17
Flying	4	4	2	4	4	4	4	4	4	4	4

read()# 每行都设置 count = 1
df['count'] = 1# 分组 求数量
df.groupby(['Type 1']).count()['count']

Type 1
Bug          69
Dark         31
Dragon       32
Electric     44
Fairy        17
Fighting     27
Fire         52
Flying        4
Ghost        32
Grass        70
Ground       32
Ice          24
Normal       98
Poison       28
Psychic      57
Rock         44
Steel        27
Water       112
Name: count, dtype: int64

read()# 每行都设置 count = 1
df['count'] = 1# 分组 求数量
df.groupby(['Type 1', 'Type 2']).count()['count']

Type 1  Type 2  
Bug     Electric     2Fighting     2Fire         2Flying      14Ghost        1..
Water   Ice          3Poison       3Psychic      5Rock         4Steel        1
Name: count, Length: 136, dtype: int64

# 读取大文件
# 分批读取数据, chunksize 行数
for df in pd.read_csv(f'{data_dir}/pandas/pokemon_data.csv', chunksize=5):
#     print('CHUNK DF:::')
#     print(df['Name']);

# 读取大文件
# 分批读取数据, chunksize 行数new_df = pd.DataFrame()
for df in pd.read_csv(f'{data_dir}/pandas/pokemon_data.csv', chunksize=5):new_df = pd.concat([new_df, df])
#     results = df.groupby(['Type 1']).count()
#     new_df = pd.concat([new_df, results])new_df

	#	Name	Type 1	Type 2	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
0	1	Bulbasaur	Grass	Poison	45	49	49	65	65	45	1	False
1	2	Ivysaur	Grass	Poison	60	62	63	80	80	60	1	False
2	3	Venusaur	Grass	Poison	80	82	83	100	100	80	1	False
3	3	VenusaurMega Venusaur	Grass	Poison	80	100	123	122	120	80	1	False
4	4	Charmander	Fire	NaN	39	52	43	60	50	65	1	False
...	...	...	...	...	...	...	...	...	...	...	...	...
795	719	Diancie	Rock	Fairy	50	100	150	100	150	50	6	True
796	719	DiancieMega Diancie	Rock	Fairy	50	160	110	160	110	110	6	True
797	720	HoopaHoopa Confined	Psychic	Ghost	80	110	60	150	130	70	6	True
798	720	HoopaHoopa Unbound	Psychic	Dark	80	160	60	170	130	80	6	True
799	721	Volcanion	Fire	Water	80	110	120	130	90	70	6	True

800 rows × 12 columns

read()# 分组 求平均数
df.groupby(['Type 1'])['Defense'].mean().plot()

在这里插入图片描述

read()# 分组 求平均数
df.groupby(['Type 1'])['Defense'].mean().plot(kind='bar')

在这里插入图片描述

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机器学习 - Pandas 练习, 常见功能查阅

Pandas

安装 pandas 库:

`pd.show_versions()`

`.describe()`

`.corr()`

皮尔逊积矩相关系数公式(Pearson product-moment correlation coefficient, PPMCC)

Kendall

Kendall’s tau

Spearman’s

Map

Apply

ApplyMap

Groupby

数据

练习

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