快速入门pandas进行数据挖掘数据分析[多维度排序、数据筛选、分组计算、透视表](一)

Python使用缩进(tab或者空格)来组织代码，而不是像其 他语言比如R、C++、Java和Perl那样用大括号。考虑使用for循 环来实现排序算法:

for x in list_values:
if x < 10:
small.append(x)
else:
bigger.append(x)

标量类型

2.3,4，null，True都是标量

变量

a=2
b='this is alibaba'
c=[1,2,456.,np.nan,c]

数据结构

#列表（list）
myList=[1,2,"hello bro",np.nan,3456225.0987]
#元组（tuple,不可修改）
myTuple=(2,3,'hey morning!',89,np.nan)
#字典（dictionary，俗称键值对）
myDictionary={'key1' :23, "key2" : "hahahh，哈哈哈", "key3" : 78}
#集合（set，集合）
myset=set({'happy','sad','sad'})

运算

a=8
b=2
c=a**b
d=a/b
d

函数(打包好的功能块)

#定义一个计算平均数的函数
def get_avg(values):
if len(values) == 0:#如果输入的list没有值
return 0 #返回0

sum_v = 0
#遍历所有值
for value in values:
#前一个和加上后一个值
sum_v = value+sum_v
return sum_v / len(values)

avg = get_avg([1, 2, 3, 4])
avg

def get_avg(values):
if len(values) == 0:#如果输入的list没有值
return 0 #返回0

sum_v = 0
#遍历所有值
for value in values:
#前一个和加上后一个值
sum_v = value+sum_v
return sum_v / len(values)

avg = get_avg([1, 2, 3, 4])
avg

循环

sum10 = 0
for i in range(1, 11):
sum10 = sum10+i
print(sum10)

sum10 = 0
for i in range(1, 11):
sum10 = sum10+i
print(i)
print(sum10)

2. 快速入门pandas

2.1 pandas核心数据结构和常用API

pandas资料下载链接：https://download.csdn.net/download/sinat_39620217/87413329

2.2 pandas 基础数据操作

导入常用的数据分析库

import numpy as np
import pandas as pd
#创建一个series
s = pd.Series([1, 3, 5, np.nan, 6, 8])
s

0 1.0
1 3.0
2 5.0
3 NaN
4 6.0
5 8.0
dtype: float64

#创建一个时间序列
dates = pd.date_range("20130101", periods=6)
dates

DatetimeIndex(['2023-02-03', '2023-02-04', '2023-02-05', '2023-02-06',
'2023-02-07', '2023-02-08'],
dtype='datetime64[ns]', freq='D')

#以时间序列为index，以“ABCD”为列明，用24个符合正态分布的随机数作为数值
df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list("ABCD"))
df

A B C D
2023-02-03 -1.688539 -0.687145 -0.087825 -0.113740
2023-02-04 -0.483402 -2.333871 -1.078778 1.786806
2023-02-05 1.154374 0.976104 0.004643 0.754242
2023-02-06 -0.005039 -0.170111 0.578378 0.604114
2023-02-07 1.923344 -1.132254 1.408248 0.101545
2023-02-08 0.876144 1.589423 1.678817 -1.271310

#另一种创建df的方法
df2 = pd.DataFrame(
{
"A": 1.0,
"B": pd.Timestamp("20130102"),
"C": pd.Series(1, index=list(range(4)), dtype="float32"),
"D": np.array([3] * 4, dtype="int32"),
"E": pd.Categorical(["test", "train", "test", "train"]),
"F": "foo",
}
)
df2

A B C D E F
0 1.0 2013-01-02 1.0 3 test foo
1 1.0 2013-01-02 1.0 3 train foo
2 1.0 2013-01-02 1.0 3 test foo
3 1.0 2013-01-02 1.0 3 train foo

#看下数据类型
df2.dtypes

A float64
B datetime64[ns]
C float32
D int32
E category
F object
dtype: object

df2.head(2)

df2.tail()

df2.sample(3)

A B C D E F
0 1.0 2013-01-02 1.0 3 test foo
1 1.0 2013-01-02 1.0 3 train foo

A B C D E F
0 1.0 2013-01-02 1.0 3 test foo
1 1.0 2013-01-02 1.0 3 train foo
2 1.0 2013-01-02 1.0 3 test foo
3 1.0 2013-01-02 1.0 3 train foo

A B C D E F
1 1.0 2013-01-02 1.0 3 train foo
2 1.0 2013-01-02 1.0 3 test foo
0 1.0 2013-01-02 1.0 3 test foo

#导入本地数据到python内存
diamonds_df=pd.read_csv('data/diamonds.csv')
diamonds_df

carat cut color clarity depth table price x y z
0 0.23 Ideal E SI2 61.5 55.0 326 3.95 3.98 2.43
1 0.21 Premium E SI1 59.8 61.0 326 3.89 3.84 2.31
2 0.23 Good E VS1 56.9 65.0 327 4.05 4.07 2.31
3 0.29 Premium I VS2 62.4 58.0 334 4.20 4.23 2.63
4 0.31 Good J SI2 63.3 58.0 335 4.34 4.35 2.75
... ... ... ... ... ... ... ... ... ... ...
53935 0.72 Ideal D SI1 60.8 57.0 2757 5.75 5.76 3.50
53936 0.72 Good D SI1 63.1 55.0 2757 5.69 5.75 3.61
53937 0.70 Very Good D SI1 62.8 60.0 2757 5.66 5.68 3.56
53938 0.86 Premium H SI2 61.0 58.0 2757 6.15 6.12 3.74
53939 0.75 Ideal D SI2 62.2 55.0 2757 5.83 5.87 3.64
53940 rows × 10 columns

#查看数据的信息或者基本情况
diamonds_df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 53940 entries, 0 to 53939
Data columns (total 10 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 carat 53940 non-null float64
1 cut 53940 non-null object
2 color 53940 non-null object
3 clarity 53940 non-null object
4 depth 53940 non-null float64
5 table 53940 non-null float64
6 price 53940 non-null int64
7 x 53940 non-null float64
8 y 53940 non-null float64
9 z 53940 non-null float64
dtypes: float64(6), int64(1), object(3)
memory usage: 4.1+ MB

#查看索引
diamonds_df.index

RangeIndex(start=0, stop=53940, step=1)


#查看列名
diamonds_df.columns

Index(['carat', 'cut', 'color', 'clarity', 'depth', 'table', 'price', 'x', 'y',
'z'],
dtype='object')

#查看数据基本情况
diamonds_df.describe()

carat depth table price x y z
count 53940.000000 53940.000000 53940.000000 53940.000000 53940.000000 53940.000000 53940.000000
mean 0.797940 61.749405 57.457184 3932.799722 5.731157 5.734526 3.538734
std 0.474011 1.432621 2.234491 3989.439738 1.121761 1.142135 0.705699
min 0.200000 43.000000 43.000000 326.000000 0.000000 0.000000 0.000000
25% 0.400000 61.000000 56.000000 950.000000 4.710000 4.720000 2.910000
50% 0.700000 61.800000 57.000000 2401.000000 5.700000 5.710000 3.530000
75% 1.040000 62.500000 59.000000 5324.250000 6.540000 6.540000 4.040000
max 5.010000 79.000000 95.000000 18823.000000 10.740000 58.900000 31.800000

#行列转换
df.T

2023-02-03 2023-02-04 2023-02-05 2023-02-06 2023-02-07 2023-02-08
A -1.688539 -0.483402 1.154374 -0.005039 1.923344 0.876144
B -0.687145 -2.333871 0.976104 -0.170111 -1.132254 1.589423
C -0.087825 -1.078778 0.004643 0.578378 1.408248 1.678817
D -0.113740 1.786806 0.754242 0.604114 0.101545 -1.271310

2.3 pandas多维度排序

#对数据进行排序
df.sort_values(by="B",ascending=False)
diamonds_df.head()

A B C D
2023-02-08 0.876144 1.589423 1.678817 -1.271310
2023-02-05 1.154374 0.976104 0.004643 0.754242
2023-02-06 -0.005039 -0.170111 0.578378 0.604114
2023-02-03 -1.688539 -0.687145 -0.087825 -0.113740
2023-02-07 1.923344 -1.132254 1.408248 0.101545
2023-02-04 -0.483402 -2.333871 -1.078778 1.786806

#按照cut和color联合排序
diamonds_df.sort_values(by=['cut','color','price'],ascending=False)

carat cut color clarity depth table price x y z
27586 2.44 Very Good J VS2 58.1 60.0 18430 8.89 8.93 5.18
27352 2.39 Very Good J VS1 59.6 60.0 17920 8.71 8.77 5.21
27185 2.44 Very Good J SI1 62.9 53.0 17472 8.58 8.62 5.41
27024 2.74 Very Good J SI2 61.5 62.0 17164 8.87 8.90 5.46
26958 2.50 Very Good J SI1 62.8 57.0 17028 8.58 8.65 5.41
... ... ... ... ... ... ... ... ... ... ...
28534 0.42 Fair D SI1 64.7 61.0 675 4.70 4.73 3.05
25695 0.40 Fair D SI1 65.1 55.0 644 4.63 4.68 3.03
10380 0.29 Fair D VS2 64.7 62.0 592 4.14 4.11 2.67
2711 0.25 Fair D VS1 61.2 55.0 563 4.09 4.11 2.51
48630 0.30 Fair D SI2 64.6 54.0 536 4.29 4.25 2.76
53940 rows × 10 columns

2.4 pandas数据筛选

#列范围
diamonds_df[["cut","depth","price"]]

cut depth price
0 Ideal 61.5 326
1 Premium 59.8 326
2 Good 56.9 327
3 Premium 62.4 334
4 Good 63.3 335

#行范围
diamonds_df[6:9]

carat cut color clarity depth table price x y z
6 0.24 Very Good I VVS1 62.3 57.0 336 3.95 3.98 2.47
7 0.26 Very Good H SI1 61.9 55.0 337 4.07 4.11 2.53
8 0.22 Fair E VS2 65.1 61.0 337 3.87 3.78 2.49

#按行范围和列具体
diamonds_df.loc[5:9, ["carat","price","x"]]

carat price x
5 0.24 336 3.94
6 0.24 336 3.95
7 0.26 337 4.07
8 0.22 337 3.87
9 0.23 338 4.00

#按行具体和列范围
#注意：具体必须要用list来承载（中括号），范围不能用中括号
diamonds_df.loc[[3,6,9], "cut":"price"]


cut color clarity depth table price
3 Premium I VS2 62.4 58.0 334
6 Very Good I VVS1 62.3 57.0 336
9 Very Good H VS1 59.4 61.0 338

#按行逻辑和列范围
diamonds_df.loc[diamonds_df.carat>0.3, ["carat","price"]]

carat price
4 0.31 335
13 0.31 344
15 0.32 345
23 0.31 353
24 0.31 353
... ... ...

#按条件筛选
#按照某列进行筛选
diamonds_df[(diamonds_df["carat"] > 0.3) & (diamonds_df["price"] < 400)]

carat cut color clarity depth table price x y z
4 0.31 Good J SI2 63.3 58.0 335 4.34 4.35 2.75
13 0.31 Ideal J SI2 62.2 54.0 344 4.35 4.37 2.71
15 0.32 Premium E I1 60.9 58.0 345 4.38 4.42 2.68
23 0.31 Very Good J SI1 59.4 62.0 353 4.39 4.43 2.62
24 0.31 Very Good J SI1 58.1 62.0 353 4.44 4.47 2.59
28271 0.32 Good D I1 64.0 54.0 361 4.33 4.36 2.78
28277 0.31 Very Good J SI1 61.9 59.0 363 4.28 4.32 2.66
28278 0.31 Very Good J SI1 62.7 59.0 363 4.29 4.32 2.70
28279 0.31 Premium J SI1 60.9 60.0 363 4.36 4.38 2.66
28280 0.31 Good J SI1 63.5 55.0 363 4.30 4.33 2.74

#筛选cut属于Premium和Good
diamonds_df[(diamonds_df['cut'].isin(['Premium','Good'])) &
(diamonds_df['carat']>0.3) & (diamonds_df['price']<400) ]

carat cut color clarity depth table price x y z
4 0.31 Good J SI2 63.3 58.0 335 4.34 4.35 2.75
15 0.32 Premium E I1 60.9 58.0 345 4.38 4.42 2.68
28271 0.32 Good D I1 64.0 54.0 361 4.33 4.36 2.78
28279 0.31 Premium J SI1 60.9 60.0 363 4.36 4.38 2.66
28280 0.31 Good J SI1 63.5 55.0 363 4.30 4.33 2.74
28284 0.32 Premium J SI1 62.2 59.0 365 4.37 4.41 2.73
34928 0.32 Good J SI1 63.2 56.0 374 4.31 4.36 2.74
34929 0.32 Good I SI2 63.4 56.0 374 4.34 4.37 2.76
34932 0.32 Good I SI2 63.1 58.0 374 4.34 4.41 2.76
34939 0.31 Good I SI1 64.3 55.0 377 4.27 4.29 2.75

2.5 pandas分组计算

#分组计算
df = pd.DataFrame(
{
"A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
"B": ["one", "one", "two", "three", "two", "two", "one", "three"],
"C": np.random.randn(8),
"D": np.random.randn(8),
}
)
df

A B C D
0 foo one -1.265302 -1.718949
1 bar one -0.814010 0.097433
2 foo two -1.359590 0.708358
3 bar three 0.562501 -2.525745
4 foo two 1.036076 0.455022
5 bar two 2.192717 -0.163239
6 foo one 0.623262 -0.632277
7 foo three -0.791469 1.801869

#按照A分组，分别计算C和D的和
df.groupby("A")[["C", "D"]].sum()

C D
A
bar 1.941208 -2.591551
foo -1.757023 0.614024

#按照多列进行分组计算
df.groupby(["A", "B"]).sum()

C D
A B
bar one -0.814010 0.097433
three 0.562501 -2.525745
two 2.192717 -0.163239
foo one -0.642040 -2.351226
three -0.791469 1.801869
two -0.323514 1.163381

#按照cut和color的组合计算平均价格
diamonds_df.groupby(by=['cut','color'])[['price']].mean().round(2).reset_index()

cut color price
0 Fair D 4291.06
1 Fair E 3682.31
2 Fair F 3827.00
3 Fair G 4239.25
4 Fair H 5135.68
5 Fair I 4685.45
6 Fair J 4975.66
7 Good D 3405.38
8 Good E 3423.64
9 Good F 3495.75
10 Good G 4123.48
11 Good H 4276.25
12 Good I 5078.53
13 Good J 4574.17

2.6 pandas透视表

#透视表
df = pd.DataFrame(
{
"甲": ["one", "one", "two", "three"] * 3,
"乙": ["A", "B", "C"] * 4,
"丙": ["foo", "foo", "foo", "bar", "bar", "bar"] * 2,
"D": np.random.randn(12),
"E": np.random.randn(12),
}
)
df

甲 乙 丙 D E
0 one A foo 0.593815 0.399765
1 one B foo 0.943989 -0.073500
2 two C foo 0.504724 0.916902
3 three A bar 1.377307 0.930002
4 one B bar 0.364403 2.430547
5 one C bar -0.392653 -0.307336
6 two A foo -0.698488 2.202757
7 three B foo -2.046343 0.562993
8 one C foo -0.570906 0.719652
9 one A bar -1.493323 0.612229
10 two B bar 1.744241 0.616304
11 three C bar 2.337644 1.568032

pd.pivot_table(df, values="D", index=["甲","乙"], columns=["丙"],aggfunc='mean')

丙 bar foo
甲 乙
one A -1.493323 0.593815
B 0.364403 0.943989
C -0.392653 -0.570906
three A 1.377307 NaN
B NaN -2.046343
C 2.337644 NaN
two A NaN -0.698488
B 1.744241 NaN
C NaN 0.504724