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python常用命令

2021-06-04 20:29  views:633  source:海是歌    

(1)打开csv文件
import pandas as pd
df=pd.read_csv(r'data/data.csv')
(2)dataframe index 重新排序
data=df.sort_index(axis=0,ascending=False)
(3)dataframe 按照某1列进行升序或者降序排列
data=df.sort(['date'],ascending=True升序,False降序)
(4)dataframe 的index重新从0开始
data=data.reset_index(drop=True)
(5)画横坐标是日期的图
import matplotlib.pyplot as plt
x=data['date']#日期是字符串形式
y=data['close price']
plt.plot_date(x,y)
(6)求标准差
import numpy as np
np.std
(7)下取整
import math
math.floor
上取整:math.ceil
(8)希尔伯特变换
from scipy import fftpack
hx= fftpack.hilbert(price)
(9)值排序
data.order()
(10)差分
data.diff(1)#1阶差分
dataframe 删除元素
data.drop(元素位置)
(11)嵌套的array处理方法
import itertools
a = [[1,2,3],[4,5,6], [7], [8,9]]
out = list(itertools.chain.from_iterable(a))
(12)dataframe修改列名
data.columns=['num','price']
(13)excel表导入以后有空行解决办法
import numpy as np
data= data.drop
(data.loc[np.isnan(data.name.values)].index)
(15)diff用法
1.是dataframe或者series格式,直接就用data.diff()
2.是list格式,先转换成转换成list格式
data=data.tolist() 然后dif=np.diff(data)
(16)dataframe中的日期type不是date格式,不能直接相加减,所以先转换成list格式
t=data.time.tolist()
date_time = datetime.datetime.strptime
(str(t),'%Y-%m-%d %H:%M:%S')
date_time=datetime.date
(date_time.year,date_time.month,date_time.day)
past= date_time - datetime.timedelta(days=n*365)
(17)符号化
np.sign
(18)字典的使用
label={'11':'TP','1-1':'FN','-11':'FP','-1-1':'TN'}
for i in range(len(data1)):
state=str(int(data1[i]))+str(int(data2[i]))
result.append(label[state])
(19)用plt画图的时候中文不显示的解决办法
from matplotlib.font_manager import FontProperties
font_set = FontProperties
(fname=r”c:windowsontssimsun.ttc”, size=15)
plt.title(u'中文', fontproperties=font_set)
(20)获取当前程序运行的时间
from time import time
time1=time()
time2=time()
print(time2-time1)
1pandas读取文件
(1)有header
filename='data.csv'
df=pd.read_csv(filename)
#(2)txt(noheader)
#指定列 分隔符:(\t)
filename='data.txt'
df=pd.read_csv(filename,sep='\t',
header=None,usecols=[0,1,3,5],names=['','','',''])
2.rename
#ridaid
df=df.rename(columns={'rid':'Rid','aid':'Aid'})
3.是否存在用isin函数
#paper_ids
df=df[df.Rid.isin(paper_ids,use_hashmap=True)]
4.去重
df=df.drop_duplicates()
5.对含有NaN的行的处理
(1)填充值
#全部填充0
df.fillna(0)
#单列填充
df['A']=df['A'].fillna(0)
(2)删除这行
df=df.dropna()



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