借助pd.date_range快速实现





## 生成 Timestamp对象 列表

生成一系列Timestamp对象，Timestamp对象是对python datetime再封装得到的，一般情况直接用起来就好了。
```python
import pandas as pd
a = list(pd.date_range(start='2021-06-21 00:00:00', end='2021-06-22 00:00:00', freq='H'))
print(a)
```

得到：

[Timestamp('2021-06-21 00:00:00', freq='H'), Timestamp('2021-06-21 01:00:00', freq='H'), Timestamp('2021-06-21 02:00:00', freq='H'), Timestamp('2021-06-21 03:00:00', freq='H'), Timestamp('2021-06-21 04:00:00', freq='H'), Timestamp('2021-06-21 05:00:00', freq='H'), Timestamp('2021-06-21 06:00:00', freq='H'), Timestamp('2021-06-21 07:00:00', freq='H'), Timestamp('2021-06-21 08:00:00', freq='H'), Timestamp('2021-06-21 09:00:00', freq='H'), Timestamp('2021-06-21 10:00:00', freq='H'), Timestamp('2021-06-21 11:00:00', freq='H'), Timestamp('2021-06-21 12:00:00', freq='H'), Timestamp('2021-06-21 13:00:00', freq='H'), Timestamp('2021-06-21 14:00:00', freq='H'), Timestamp('2021-06-21 15:00:00', freq='H'), Timestamp('2021-06-21 16:00:00', freq='H'), Timestamp('2021-06-21 17:00:00', freq='H'), Timestamp('2021-06-21 18:00:00', freq='H'), Timestamp('2021-06-21 19:00:00', freq='H'), Timestamp('2021-06-21 20:00:00', freq='H'), Timestamp('2021-06-21 21:00:00', freq='H'), Timestamp('2021-06-21 22:00:00', freq='H'), Timestamp('2021-06-21 23:00:00', freq='H'), Timestamp('2021-06-22 00:00:00', freq='H')]

## 生成 str对象 列表

```python
import pandas as pd
a = list(pd.date_range(start='2021-06-21 00:00:00', end='2021-06-22 00:00:00', freq='H'))
a=list(map(str,a))
print(a)
```

得到：

['2021-06-21 00:00:00', '2021-06-21 01:00:00', '2021-06-21 02:00:00', '2021-06-21 03:00:00', '2021-06-21 04:00:00', '2021-06-21 05:00:00', '2021-06-21 06:00:00', '2021-06-21 07:00:00', '2021-06-21 08:00:00', '2021-06-21 09:00:00', '2021-06-21 10:00:00', '2021-06-21 11:00:00', '2021-06-21 12:00:00', '2021-06-21 13:00:00', '2021-06-21 14:00:00', '2021-06-21 15:00:00', '2021-06-21 16:00:00', '2021-06-21 17:00:00', '2021-06-21 18:00:00', '2021-06-21 19:00:00', '2021-06-21 20:00:00', '2021-06-21 21:00:00', '2021-06-21 22:00:00', '2021-06-21 23:00:00', '2021-06-22 00:00:00']

## 生成 python datetime对象 列表

```python
import pandas as pd
a = list(pd.date_range(start='2021-06-21 00:00:00', end='2021-06-22 00:00:00', freq='H'))
a=list(map(lambda x:x.to_pydatetime(),a))
print(a)
```

得到：

```python
[datetime.datetime(2021, 6, 21, 0, 0), datetime.datetime(2021, 6, 21, 1, 0), datetime.datetime(2021, 6, 21, 2, 0), datetime.datetime(2021, 6, 21, 3, 0), datetime.datetime(2021, 6, 21, 4, 0), datetime.datetime(2021, 6, 21, 5, 0), datetime.datetime(2021, 6, 21, 6, 0), datetime.datetime(2021, 6, 21, 7, 0), datetime.datetime(2021, 6, 21, 8, 0), datetime.datetime(2021, 6, 21, 9, 0), datetime.datetime(2021, 6, 21, 10, 0), datetime.datetime(2021, 6, 21, 11, 0), datetime.datetime(2021, 6, 21, 12, 0), datetime.datetime(2021, 6, 21, 13, 0), datetime.datetime(2021, 6, 21, 14, 0), datetime.datetime(2021, 6, 21, 15, 0), datetime.datetime(2021, 6, 21, 16, 0), datetime.datetime(2021, 6, 21, 17, 0), datetime.datetime(2021, 6, 21, 18, 0), datetime.datetime(2021, 6, 21, 19, 0), datetime.datetime(2021, 6, 21, 20, 0), datetime.datetime(2021, 6, 21, 21, 0), datetime.datetime(2021, 6, 21, 22, 0), datetime.datetime(2021, 6, 21, 23, 0), datetime.datetime(2021, 6, 22, 0, 0)]
```

# 转换

 pd.Timestamp 转 datetime.datetime  ：
```python
pd.Timestamp("2021-06-06").to_pydatetime()
```

datetime.datetime  转 pd.Timestamp：

```python
pd.to_datetime(datetime.datetime.now())
```

python 生成一段日期列表 pd.date_range Timestamp 转 datetime datetime 转 Timestamp

（1）本质核心是多进程里执行os.system("wget filename")，开17个进程的进程池意味着同时在下载17个文件。





（2）将指令全部放列表needgfsfile里面。

```python
import datetime
import multiprocessing
import os
def downloadgfs(filename):
    os.system(filename)
start_hour = 12
yesterday = (datetime.datetime.now() - datetime.timedelta(days=1)).replace(hour=start_hour, microsecond=0)
ymdstr = f"{str(yesterday.year).zfill(4)}{str(yesterday.month).zfill(2)}{str(yesterday.day).zfill(2)}{str(start_hour).zfill(2)}"
if not os.path.exists(ymdstr):
    os.mkdir(ymdstr)
needgfsfile = [
    f"wget ftp://ftpprd.ncep.noaa.gov/pub/data/nccf/com/gfs/prod/gfs.{ymdstr[:-2]}/{ymdstr[-2:]}/atmos/gfs.t{ymdstr[-2:]}z.pgrb2.0p50.f{str(i * 24).zfill(3)} -P {ymdstr}/"
    for i in range(17)]
p = multiprocessing.Pool(processes=17)  # 进to程池
_ = [p.apply_async(func=downloadgfs, args=(i,)) for i in needgfsfile]
p.close()
p.join()
```

python wget 并行下载多个文件 同时下载多个文件 ftp文件 多进程

## 1 加解释器后加路径





![在这里插入图片描述](/static/img/images/00435.png)

## 2 配置

![在这里插入图片描述](/static/img/images/00436.png)

这样点一下后切到用户目录作为Root path

![在这里插入图片描述](/static/img/images/00437.png)

手动修改这里：

![在这里插入图片描述](/static/img/images/00438.png)

## 3 第一次需要手动上传

![在这里插入图片描述](/static/img/images/00439.png)

Pycharm Deployment ssh 解释器 文件同步设置

[https://stackoverflow.com/questions/19237512/rbf-interpolation-linalgerror-singular-matrix](https://stackoverflow.com/questions/19237512/rbf-interpolation-linalgerror-singular-matrix)

RBF interpolation: LinAlgError: singular matrix 热力图

[https://pytorch.org/get-started/previous-versions/](https://pytorch.org/get-started/previous-versions/)





```handlebars
# CUDA 10.1
conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=10.1 -c pytorch
```

pytorch 安装

北京外国语大学开源软件镜像站





```handlebars
channels:
  - defaults
show_channel_urls: true
channel_alias: https://mirrors.bfsu.edu.cn/anaconda
default_channels:
  - https://mirrors.bfsu.edu.cn/anaconda/pkgs/main
  - https://mirrors.bfsu.edu.cn/anaconda/pkgs/free
  - https://mirrors.bfsu.edu.cn/anaconda/pkgs/r
  - https://mirrors.bfsu.edu.cn/anaconda/pkgs/pro
  - https://mirrors.bfsu.edu.cn/anaconda/pkgs/msys2
custom_channels:
  conda-forge: https://mirrors.bfsu.edu.cn/anaconda/cloud
  msys2: https://mirrors.bfsu.edu.cn/anaconda/cloud
  bioconda: https://mirrors.bfsu.edu.cn/anaconda/cloud
  menpo: https://mirrors.bfsu.edu.cn/anaconda/cloud
  pytorch: https://mirrors.bfsu.edu.cn/anaconda/cloud
  simpleitk: https://mirrors.bfsu.edu.cn/anaconda/cloud
```

上海交通大学开源软件镜像站

```handlebars
channels:
  - defaults
show_channel_urls: true
channel_alias: https://anaconda.mirrors.sjtug.sjtu.edu.cn/
default_channels:
  - https://anaconda.mirrors.sjtug.sjtu.edu.cn/pkgs/main
  - https://anaconda.mirrors.sjtug.sjtu.edu.cn/pkgs/free
  - https://anaconda.mirrors.sjtug.sjtu.edu.cn/pkgs/mro
  - https://anaconda.mirrors.sjtug.sjtu.edu.cn/pkgs/msys2
  - https://anaconda.mirrors.sjtug.sjtu.edu.cn/pkgs/pro
  - https://anaconda.mirrors.sjtug.sjtu.edu.cn/pkgs/r
custom_channels:
  conda-forge: https://anaconda.mirrors.sjtug.sjtu.edu.cn/conda-forge
  soumith: https://anaconda.mirrors.sjtug.sjtu.edu.cn/cloud/soumith
  bioconda: https://anaconda.mirrors.sjtug.sjtu.edu.cn/cloud/bioconda
  menpo: https://anaconda.mirrors.sjtug.sjtu.edu.cn/cloud/menpo
  viscid-hub: https://anaconda.mirrors.sjtug.sjtu.edu.cn/cloud/viscid-hub
  atztogo: https://anaconda.mirrors.sjtug.sjtu.edu.cn/cloud/atztogo
```

清华大学开源软件镜像站

```handlebars
channels:
  - defaults
show_channel_urls: true
default_channels:
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/msys2
custom_channels:
  conda-forge: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  msys2: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  bioconda: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  menpo: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  pytorch: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  simpleitk: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
```

conda 镜像站

Python





```handlebars
import cmath
import json
import requests
from geopandas import GeoDataFrame,GeoSeries
from shapely.geometry import shape, Polygon, MultiPoint,Point
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def get_zjk_df():
    """
    张家口
    :return:
    """
    url = "https://geo.datav.aliyun.com/areas_v2/bound/130700.json"
    r = requests.get(url)
    b = shape(r.json()['features'][0]['geometry'])
    geodf = GeoDataFrame([{'geometry': b}], crs="EPSG:4326")
    return geodf
a="""
115.503 40.419
115.63025 40.22925
115.72225 40.08925
115.78275 39.96275
115.824 39.851
115.862 39.74475
115.899 39.63675
115.9335 39.5265
115.95975 39.4155
115.975 39.306
115.974 39.20675
115.959 39.12975
115.93325 39.07725
115.89825 39.04325
115.86125 39.028
115.827 39.03175
115.79725 39.04425
115.781 39.0655
115.7795 39.09725
115.78725 39.13525
115.80775 39.1865
115.851 39.24625
115.9285 39.312
116.04525 39.3845
116.19325 39.46125
116.353 39.54275
116.5025 39.6265
116.64025 39.70825
116.77675 39.78975
116.92275 39.86725
117.0855 39.945
117.2595 40.0265
117.44625 40.115
117.6475 40.2025
117.856 40.2845
118.06225 40.355
118.263 40.4185
118.449 40.48875
118.63075 40.5655
118.8145 40.6455
119.0 40.72825
119.1935 40.814
119.3765 40.912
119.56275 41.02025
119.75525 41.13875
119.9655 41.26175
120.18925 41.3805
120.42075 41.491
120.66425 41.592"""
a=a.splitlines()
a=list(filter(lambda x:len(x)>5,a))
lon=list(map(lambda x:float(x.split()[0]),a))
lat=list(map(lambda x:float(x.split()[1]),a))
geodf = get_zjk_df()
ax=geodf.plot(color='white', edgecolor='black')
fblock=[]
for i in zip(lon,lat):
    fblock.append(Point(i))
fblock_df = GeoSeries(fblock)
fblock_df.plot(ax=ax, marker='o', color='red', markersize=5)
# 显示与保存
plt.title("zjk")
plt.xlabel('lon')
plt.ylabel('lat')
plt.grid()
plt.savefig("./nihao.svg")
plt.show()
```

geopandas python 画地图和路径点

装载自：[https://www.debugger.wiki/article/html/1577444402436939](https://www.debugger.wiki/article/html/1577444402436939)





![在这里插入图片描述](/static/img/images/00479.png)

```handlebars
import numpy as np
import pandas as pd
def detect_outliers(data, threshold=3):
    """离群点检测"""
    mean_d = np.mean(data)
    std_d = np.std(data)
    outliers = []
    for y in data:
        z_score = (y - mean_d) / std_d
        if np.abs(z_score) > threshold:
            outliers.append(y)
    return outliers
```

![在这里插入图片描述](/static/img/images/00480.png)

```handlebars
def detect_outliers(sr):
    q1 = sr.quantile(0.25)
    q3 = sr.quantile(0.75)
    iqr = q3-q1 #Interquartile range
    fence_low  = q1-1.5*iqr
    fence_high = q3+1.5*iqr
    outliers = sr.loc[(sr < fence_low) | (sr > fence_high)]
    return outliers
```

python 数据 分析 数据处理 离群点检测

df里都是数字的：




```handlebars
df[df<0]=np.nan
```

不然：
```handlebars
import numpy as np
import pandas as pd
import datetime
df = pd.DataFrame({'a': [0, -1, 2],
                   'b': [-3, 2, 1],
                   'c': ['foo', 'goo', 'bar'],
                   'd': [datetime.datetime.now(), datetime.datetime.now(), datetime.datetime.now()]})
df[df._get_numeric_data() < 0] = np.nan
print(df)
```

结果：

![在这里插入图片描述](/static/img/images/00481.png)

python pandas 表里所有负数赋为空

读取




```handlebars
stationdf = pd.read_excel('./jzk_point_city/zjk_point_excel.xlsx', engine='openpyxl', index_col=0)
```

写入
```handlebars
from pandas import ExcelWriter
with ExcelWriter('./zjk_point_excel.xlsx') as writer:
    df.to_excel(writer)
```