[Python知识库]2021-09-01 机器学习数据处理之pandas基础

numpy能够帮助我们处理数值，但是pandas除了处理数值之外(基于numpy)，还能够帮助我们处理其他类型的数据（字符串、时间序列等等）

Series

import pandas as pd
import numpy as np
import string

t=pd.Series(np.arange(10),index=list(string.ascii_uppercase[:10]))
t

A    0
B    1
C    2
D    3
E    4
F    5
G    6
H    7
I    8
J    9
dtype: int64

type(t)

pandas.core.series.Series

a={string.ascii_uppercase[i]:i for i in range(10)}
a

{'A': 0,
 'B': 1,
 'C': 2,
 'D': 3,
 'E': 4,
 'F': 5,
 'G': 6,
 'H': 7,
 'I': 8,
 'J': 9}

pd.Series(a)

A    0
B    1
C    2
D    3
E    4
F    5
G    6
H    7
I    8
J    9
dtype: int64

pd.Series(a,index=list(string.ascii_uppercase[5:15]))

F    5.0
G    6.0
H    7.0
I    8.0
J    9.0
K    NaN
L    NaN
M    NaN
N    NaN
O    NaN
dtype: float64

t

A    0
B    1
C    2
D    3
E    4
F    5
G    6
H    7
I    8
J    9
dtype: int64

t["F"]

5

t[1]

1

t[[2,4,5]]

C    2
E    4
F    5
dtype: int64

t[["A","G"]]

A    0
G    6
dtype: int64

t.index

Index(['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J'], dtype='object')

t.values

array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

type(t.index)

pandas.core.indexes.base.Index

type(t.values)

numpy.ndarray

DataFrame

tdf=pd.DataFrame(np.arange(12).reshape((3,4)))
tdf

tdf.shape # 行 列

(3, 4)

tdf.dtypes  #列的数据类型

0    int64
1    int64
2    int64
3    int64
dtype: object

tdf.ndim  #数据维度

2

tdf.index #行索引

RangeIndex(start=0, stop=3, step=1)

tdf.columns  #列索引

RangeIndex(start=0, stop=4, step=1)

tdf.values  #对象值

array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])

tdf.head(2)  #显示头2行，默认5行

tdf.tail(3)  #显示末尾3行，默认5行

tdf.info() # 相关信息概览：

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 4 columns):
 #   Column  Non-Null Count  Dtype
---  ------  --------------  -----
 0   0       3 non-null      int64
 1   1       3 non-null      int64
 2   2       3 non-null      int64
 3   3       3 non-null      int64
dtypes: int64(4)
memory usage: 224.0 bytes

#pandas读取csv中的文件
df = pd.read_csv("./day04/code/dogNames2.csv")
print(df[(800<df["Count_AnimalName"])|(df["Count_AnimalName"]<1000)],'\n')
# print('df.head:\n',df.head(),'\n\n')
print(df.info())

      Row_Labels  Count_AnimalName
0              1                 1
1              2                 2
2          40804                 1
3          90201                 1
4          90203                 1
...          ...               ...
16215      37916                 1
16216      38282                 1
16217      38583                 1
16218      38948                 1
16219      39743                 1

[16220 rows x 2 columns] 

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 16220 entries, 0 to 16219
Data columns (total 2 columns):
 #   Column            Non-Null Count  Dtype 
---  ------            --------------  ----- 
 0   Row_Labels        16217 non-null  object
 1   Count_AnimalName  16220 non-null  int64 
dtypes: int64(1), object(1)
memory usage: 253.6+ KB
None

DataFrame是由Series组成的

df[:20] #方括号里面写数组表示对行进行操作

df["Count_AnimalName"]   #方括号里面写字符串表示对列操作

0        1
1        2
2        1
3        1
4        1
        ..
16215    1
16216    1
16217    1
16218    1
16219    1
Name: Count_AnimalName, Length: 16220, dtype: int64

type(df["Count_AnimalName"])

pandas.core.series.Series

pandas的loc和iloc

df.loc[1,"Row_Labels"]

'2'

df.loc[[1,2,4,5],["Row_Labels","Count_AnimalName"]]

df.loc[[4],["Row_Labels","Count_AnimalName"]]

df.loc[1:8,["Row_Labels","Count_AnimalName"]]

df.iloc[1:6,[0,1]]

df[df["Count_AnimalName"]>999]

df[(df["Count_AnimalName"]>700)&(df["Row_Labels"].str.len()>=4)]
# 不同条件之间需要用小括号 括起来

file_path51 = "./day05/code/IMDB-Movie-Data.csv"
df51 = pd.read_csv(file_path51)

# print(df51.info())

print(df51.head(1))

#获取平均评分
print('rating-meaning:',df51["Rating"].mean())

#导演的人数
# print(len(set(df["Director"].tolist())))
# print(len(df51["Director"].unique()))

#获取演员的人数
temp_actors_list = df51["Actors"].str.split(", ").tolist()
actors_list = [i for j in temp_actors_list for i in j]
actors_num = len(set(actors_list))
print(actors_num)

   Rank                    Title                    Genre  \
0     1  Guardians of the Galaxy  Action,Adventure,Sci-Fi   

                                         Description    Director  \
0  A group of intergalactic criminals are forced ...  James Gunn   

                                              Actors  Year  Runtime (Minutes)  \
0  Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...  2014                121   

   Rating   Votes  Revenue (Millions)  Metascore  
0     8.1  757074              333.13       76.0  
rating-meaning: 6.723199999999999
2015

import pandas as pd
from matplotlib import pyplot as plt

file_path52 = "./day05/code/starbucks_store_worldwide.csv"

df52 = pd.read_csv(file_path52)

#使用matplotlib呈现出店铺总数排名前10的国家
#准备数据
data1 = df52.groupby(by="Country").count()["Brand"].sort_values(ascending=False)[:10] # ascending-false降序

print(data1,'\n')
_x = data1.index
_y = data1.values

#画图
plt.figure(figsize=(20,8),dpi=80)

plt.bar(range(len(_x)),_y)

plt.xticks(range(len(_x)),_x)

plt.show()

Country
US    13608
CN     2734
CA     1468
JP     1237
KR      993
GB      901
MX      579
TW      394
TR      326
PH      298
Name: Brand, dtype: int64 

• 重采样：指的是将时间序列从一个频率转化为另一个频率进行处理的过程，将高频率数据转化为低频率数据为降采样，低频率转化为高频率为升采样

• pandas提供了一个resample的方法来帮助我们实现频率转化

# coding=utf-8
import pandas as pd
from matplotlib import  pyplot as plt
file_path61 = "./day06/code/PM2.5/BeijingPM20100101_20151231.csv"

df61 = pd.read_csv(file_path61)

#把分开的时间字符串通过periodIndex的方法转化为pandas的时间类型
period = pd.PeriodIndex(year=df61["year"],month=df61["month"],day=df61["day"],hour=df61["hour"],freq="H")
df61["datetime"] = period
print('head():\n',df61.head(),'\n')

#把datetime 设置为索引
df61.set_index("datetime",inplace=True)

#进行降采样
df61 = df61.resample("7D").mean()
print('resample:\n',df61.head())
#处理缺失数据，删除缺失数据
# print(df61["PM_US Post"])

data  =df61["PM_US Post"]
data_china = df61["PM_Nongzhanguan"]

print(data_china.head())
#画图
_x = data.index
_x = [i.strftime("%Y%m%d") for i in _x]
_x_china = [i.strftime("%Y%m%d") for i in data_china.index]
print(len(_x_china),len(_x_china))
_y = data.values
_y_china = data_china.values


plt.figure(figsize=(20,8),dpi=80)

plt.plot(range(len(_x)),_y,label="US_POST",alpha=0.7)
plt.plot(range(len(_x_china)),_y_china,label="CN_POST",alpha=0.7)

plt.xticks(range(0,len(_x_china),10),list(_x_china)[::10],rotation=45)

plt.legend(loc="best")

plt.show()

head():
    No  year  month  day  hour  season  PM_Dongsi  PM_Dongsihuan  \
0   1  2010      1    1     0       4        NaN            NaN   
1   2  2010      1    1     1       4        NaN            NaN   
2   3  2010      1    1     2       4        NaN            NaN   
3   4  2010      1    1     3       4        NaN            NaN   
4   5  2010      1    1     4       4        NaN            NaN   

   PM_Nongzhanguan  PM_US Post  DEWP  HUMI    PRES  TEMP cbwd    Iws  \
0              NaN         NaN -21.0  43.0  1021.0 -11.0   NW   1.79   
1              NaN         NaN -21.0  47.0  1020.0 -12.0   NW   4.92   
2              NaN         NaN -21.0  43.0  1019.0 -11.0   NW   6.71   
3              NaN         NaN -21.0  55.0  1019.0 -14.0   NW   9.84   
4              NaN         NaN -20.0  51.0  1018.0 -12.0   NW  12.97   

   precipitation  Iprec          datetime  
0            0.0    0.0  2010-01-01 00:00  
1            0.0    0.0  2010-01-01 01:00  
2            0.0    0.0  2010-01-01 02:00  
3            0.0    0.0  2010-01-01 03:00  
4            0.0    0.0  2010-01-01 04:00   

resample:
                No    year     month        day  hour  season  PM_Dongsi  \
datetime                                                                  
2010-01-01   84.5  2010.0  1.000000   4.000000  11.5     4.0        NaN   
2010-01-08  252.5  2010.0  1.000000  11.000000  11.5     4.0        NaN   
2010-01-15  420.5  2010.0  1.000000  18.000000  11.5     4.0        NaN   
2010-01-22  588.5  2010.0  1.000000  25.000000  11.5     4.0        NaN   
2010-01-29  756.5  2010.0  1.571429  14.285714  11.5     4.0        NaN   

            PM_Dongsihuan  PM_Nongzhanguan  PM_US Post       DEWP       HUMI  \
datetime                                                                       
2010-01-01            NaN              NaN   71.627586 -18.255952  54.395833   
2010-01-08            NaN              NaN   69.910714 -19.035714  49.386905   
2010-01-15            NaN              NaN  163.654762 -12.630952  57.755952   
2010-01-22            NaN              NaN   68.069307 -17.404762  34.095238   
2010-01-29            NaN              NaN   53.583333 -17.565476  34.928571   

                   PRES       TEMP        Iws  precipitation     Iprec  
datetime                                                                
2010-01-01  1027.910714 -10.202381  43.859821       0.066667  0.786905  
2010-01-08  1030.035714 -10.029762  45.392083       0.000000  0.000000  
2010-01-15  1030.386905  -4.946429  17.492976       0.000000  0.000000  
2010-01-22  1026.196429  -2.672619  54.854048       0.000000  0.000000  
2010-01-29  1025.273810  -2.083333  26.625119       0.000000  0.000000  
datetime
2010-01-01   NaN
2010-01-08   NaN
2010-01-15   NaN
2010-01-22   NaN
2010-01-29   NaN
Freq: 7D, Name: PM_Nongzhanguan, dtype: float64
313 313