英文:
Plotting events as a single bar barplot
问题
# Set the figure size for better visualizationplt.figure(figsize=(10, 6))# Create a horizontal bar chart with start time on the y-axis and duration on the x-axisplt.barh(df['start'], df['duration'], height=0.5)# Set the y-axis label to be the start dateplt.ylabel('Start Date')# Set the x-axis label to be the duration in minutesplt.xlabel('Duration (minutes)')# Customize the x-axis ticks and labels for better readabilityplt.xticks(range(0, int(df['duration'].max()) + 1, 6))# Add vertical lines at 6-hour intervalsfor i in range(6, int(df['duration'].max()) + 1, 6):plt.axvline(x=i, color='gray', linestyle='--', linewidth=0.8)# Show the plotplt.show()
英文:
I have done some network testing, and have ended up with a CSV-file representing the dropouts in connection on this format:
seqstart,seqend,start date,start time,end date,end time,latency(ms)23,58,20/02/2023,17:38:12.524622,20/02/2023,17:38:30.024620,17.49999883,144,20/02/2023,17:38:42.524619,20/02/2023,17:39:13.024569,30.49995177,187,20/02/2023,17:39:29.524621,20/02/2023,17:39:34.524625,5.000004188,217,20/02/2023,17:39:35.024591,20/02/2023,17:39:49.524621,14.500034011,4044,20/02/2023,18:11:26.524624,20/02/2023,18:11:43.024625,16.5000014131,4163,20/02/2023,18:12:26.524627,20/02/2023,18:12:42.524625,15.9999984191,4223,20/02/2023,18:12:56.524627,20/02/2023,18:13:12.524627,16.04461,4523,20/02/2023,18:15:11.524626,20/02/2023,18:15:42.524626,31.016671,16733,20/02/2023,19:56:56.524634,20/02/2023,19:57:27.524628,30.999994
I want to illustrate this on a format similar to this:
Together with ChatGPT I managed to get this, with the code:
import pandas as pdimport matplotlib.pyplot as plt# Load the CSV file into a pandas DataFramedf = pd.read_csv('filename.csv')# Combine start-date and start-time into a single datetime columndf['start'] = pd.to_datetime(df['start date'] + ' ' + df['start time'], format='%d/%m/%Y %H:%M:%S.%f')# Combine end-date and end-time into a single datetime columndf['end'] = pd.to_datetime(df['end date'] + ' ' + df['end time'], format='%d/%m/%Y %H:%M:%S.%f')# Calculate the duration of each event in minutesdf['duration'] = (df['end'] - df['start']).dt.total_seconds() / 60# Sort the events by start timedf = df.sort_values(by='start')# Create a horizontal bar chart with start time on the y-axis and duration on the x-axisplt.barh(df['start'], df['duration'], height=0.5)# Set the y-axis label to be the start dateplt.ylabel('Start Date')# Set the x-axis label to be the duration in minutesplt.xlabel('Duration (minutes)')# Show the plotplt.show()
How can I alter this code to get the illustration showed at the top? I have tried asking ChatGPT even further, but it did not give me any results
Optionally - Since the data is taken over a period of days. To make it more readable, maybe it could be possible to display 6 or 12 hours on the X-axis, and then make a new line/pipe? Like shown below:
答案1
得分: 1
这假设您能够重新构建数据框，以便对于每一天，交替包含无丢失和有丢失的持续时间。有比丢失最多的那一天少的天数将用 np.nan 值填充。注意，每行的所有持续时间应该加起来等于 24 小时。
import pandas as pdimport numpy as npfrom itertools import cycleimport matplotlib.pyplot as pltdf = pd.DataFrame([[10, 2, 1, 1, 10],[10, 3, 11, np.nan, np.nan],], index=["day1", "day2"])color_cycles = cycle(["green", "red"])colors = [next(color_cycles) for _ in range(len(df.iloc[0]))]df.plot.barh(stacked=True, color=colors)plt.legend([])
英文:
This assumes you manage to rebuild your dataframe to contain for each day alternating the duration without dopouts and the duration with dropouts. The days with fewer dropouts than the day with most dropouts are filled with np.nan values. Note, All durations per row should add up to 24h.
import pandas as pdimport numpy as npfrom itertools import cycleimport matplotlib.pyplot as pltdf = pd.DataFrame([[10, 2, 1, 1, 10],[10, 3, 11, np.nan, np.nan],], index=["day1", "day2"])color_cycles = cycle(["green", "red"])colors = [next(colors) for _ in range(len(df[0]))]df.plot.barh(stacked=True, color=colors)plt.legend([])
答案2
得分: 1
以下是代码部分的翻译：
import pandas as pdfrom datetime import timedeltaimport numpy as npimport matplotlib.pyplot as pltdf = pd.read_csv("data.csv",parse_dates=["start date", "end date"])# 为开始时间添加新列df["start minute of the day"] = df["start time"].apply(lambda x: timedelta(hours=int(x.split(":")[0]),minutes=int(x.split(":")[1]),seconds=float(x.split(":")[2])).total_seconds() / 60)# 为结束时间添加新列df["end minute of the day"] = df["end time"].apply(lambda x: timedelta(hours=int(x.split(":")[0]),minutes=int(x.split(":")[1]),seconds=float(x.split(":")[2])).total_seconds() / 60)# 有序的不同日期列表days = list(sorted(df["start date"].unique()))# 创建一个图像bar_height = 20 # 像素minute_width = 1 # 像素color_green = np.array([0, 191, 84])color_red = np.array([255, 20, 64])minimum_error_width = 50 # 根据需要减少img_width = minute_width * 24 * 60 # 一整天img_height = bar_height * len(days)img = np.ones((img_height, img_width, 3), np.uint8)# 填充所有的绿色img = img * color_greenfor img_row_idx, day in enumerate(days):df_filtered = df[df['start date'] == day]for _, row in df_filtered.iterrows():# 如果选择的时间间隔不是分钟，根据需要调整 roundstart_index = int(row["start minute of the day"])end_index = int(row["end minute of the day"])end_index = min(max(end_index, start_index + minimum_error_width), img_width)# 填充红色img[img_row_idx * bar_height:(img_row_idx + 1) * bar_height,start_index:end_index] = color_redplt.imshow(img)plt.yticks(ticks=np.linspace(bar_height / 2, bar_height * len(days) - bar_height / 2, num=len(days)),labels=[str(d)[:10] for d in days])plt.xlabel("一天中的分钟")plt.ylabel("日期")plt.savefig("output.jpg")
请注意，这是代码的翻译部分，不包括注释。
英文:
This is a version that works with images.
Please note: There is a minimum error width, because your errors are so short in duration, that they will be hardly ever visible. Adjust to your needs. Also: If an error starts at day one and ends at day two, the error is only shown for day one.
import pandas as pdfrom datetime import timedeltaimport numpy as npimport matplotlib.pyplot as pltdf = pd.read_csv("data.csv",parse_dates=["start date", "end date"])# add new column for startdf["start minute of the day"] = df["start time"].apply(lambda x: timedelta(hours=int(x.split(":")[0]),minutes=int(x.split(":")[1]),seconds=float(x.split(":")[2])).total_seconds() / 60)# add new column for enddf["end minute of the day"] = df["end time"].apply(lambda x: timedelta(hours=int(x.split(":")[0]),minutes=int(x.split(":")[1]),seconds=float(x.split(":")[2])).total_seconds() / 60)# list of (ordered) distinct daysdays = list(sorted(df["start date"].unique()))# create an imagebar_height = 20 # pxminute_width = 1 # pxcolor_green = np.array([0, 191, 84])color_red = np.array([255, 20, 64])minimum_error_width = 50 # reduce to your needimg_width = minute_width * 24 * 60 # one full dayimg_height = bar_height * len(days)img = np.ones((img_height, img_width, 3), np.uint8)# fill all greenimg = img * color_greenfor img_row_idx, day in enumerate(days):df_filtered = df[df['start date'] == day]for _, row in df_filtered.iterrows():# if you choose other intervals than minutes, adjust round accordinglystart_index = int(row["start minute of the day"])end_index = int(row["end minute of the day"])end_index = min(max(end_index, start_index+minimum_error_width), img_width)# fill redimg[img_row_idx*bar_height:(img_row_idx+1)*bar_height,start_index:end_index] = color_redplt.imshow(img)plt.yticks(ticks=np.linspace(bar_height/2, bar_height*len(days)-bar_height/2, num=len(days)),labels=[str(d)[:10] for d in days])plt.xlabel("minute of day")plt.ylabel("day")plt.savefig("output.jpg")
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