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python - 在日期时间中每 10 分钟绘制一次

问题描述

我使用的“df”每个都有多行datetime。我想datetime每 10 分钟绘制一个所有坐标的散点图。每个位置都有一个数据条目，每 10 分钟df_data

如果我手动输入时间，它可以工作，t_list = [datetime(2017, 12, 23, 06, 00, 00), datetime(2017, 12, 23, 06, 10, 00), datetime(2017, 12, 23, 06, 20, 00)]但我想用使用日期的东西替换它，df这样我就可以将它用于多个数据集。

import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
import numpy as np

df_data = pd.read_csv('C:\data.csv')
df_data['datetime'] = pd.to_datetime(df_data['TimeStamp'] )
df = df_data[(df_data['datetime']>= datetime(2017, 12, 23, 06,00, 00)) &
         (df_data['datetime']< datetime(2017, 12, 23, 07, 00, 00))]

##want a time array for all of the datetimes in the df
t_list = [datetime(2017, 12, 23, 06, 00, 00), datetime(2017, 12, 23, 06, 10, 00), 
datetime(2017, 12, 
23, 06, 20, 00)]

for t in t_list:
    t_end = t + timedelta(minutes = 10)
    t_text = t.strftime("%d-%b-%Y (%H:%M)")

    #boolean indexing with multiple conditions, you should wrap each single condition in brackets
    df_t = df[(df['datetime']>=t) & (df['datetime']<t_end)]

    #get data into variable
    ws = df_t['Sp_mean']
    lat = df_t['x']
    lon = df_t['y']
    col = 0.75

    #calc min/max for setting scale on images
    min_ws = df['Sp_mean'].min()
    max_ws = df['Sp_mean'].max()

    plt.figure(figsize=(15,10))
    plt.scatter(lon, lat, c=ws,s=300, vmin=min_ws, vmax=max_ws)  
    plt.title('event' + t_text,fontweight = 'bold',fontsize=18)
    plt.show()

我已经尝试了几种尝试将副本复制datetime为可迭代列表的方法，但这些方法并没有给我想要的结果，最新的如下： 

date_arrray = np.arange(np.datetime64(df['datetime']))
df['timedelta'] = pd.to_timedelta(df['datetime'])

示例数据集

在此处输入图像描述

标签： pythonpython-2.7datetimefor-loop

解决方案

接缝你不熟悉的熊猫。您应该检查重采样功能。
让我们df_data成为您的原始数据：

# make a DatetimeIndex and resample it to 10-Min interval
df_data.index = pd.to_datetime(df_data['TimeStamp'])
resampled_data = df_data.resample('10Min')

# loop it:
min_ws = df['Sp_mean'].min()
max_ws = df['Sp_mean'].max()
col = 0.75
for start_time, sampled_df in resampled_data:
    ws = sampled_df['Sp_mean']
    lat = sampled_df['x']
    lon = sampled_df['y']
    plt.figure(figsize=(15,10))
    plt.scatter(lon, lat, c=ws,s=300, vmin=min_ws, vmax=max_ws)  
    plt.title('event' + start_time.strftime('%Y-%m-%d %H:%M:%S'),fontweight = 'bold',fontsize=18)
    plt.show()

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