我正在使用的數據是對話消息日志。我有一個熊貓數據幀，以日期戳作為索引，還有兩列;一個用于“發件人”，一個用于“消息”。我只是試圖繪制一個隨時間變化的消息堆棧圖。我實際上并不需要消息的內容，因此我按如下方式清理了數據：dfgrouped = df.groupby(["sender"])dfgrouped[["sender"]].resample("D").count()這將提供按對話中的每個發件人分組的數據幀，其中 DateTime 作為索引，并在給定日期發送的消息數。dfgrouped[["sender"]].get_group("Joe Bloggs").resample("D").count()...將給出一個只有一個用戶的數據幀，并且他們的消息每天計數。我想知道如何使用 matplotlib 來繪制一個堆棧圖，其中每個“發送方”是一條不同的線。我也無法通過ax.stackplot(dfgrouped[["sender"]].resample("D").count())或通過循環：for sender in df["sender"].unique(): axs[i].stackplot(dfgrouped[["sender"]].get_group(sender).resample("D").count()
1 回答
神不在的星期二
TA貢獻1963條經驗 獲得超6個贊
您可以使用熊貓自己的堆棧圖函數，df.plot.area（）。這是 Matplotlib 函數的包裝器，用作數據幀上的方法。您只需要將數據保持在正確的形狀。通過您的分組和計數操作，您幾乎就在那里：
import pandas as pd
df = pd.DataFrame({'sender': [
'Person 2', 'Person 1', 'Person 2', 'Person 1', 'Person 2', 'Person 1', 'Person 2',
'Person 1', 'Person 1', 'Person 2', 'Person 1', 'Person 2', 'Person 1', 'Person 2',
'Person 2', 'Person 2', 'Person 2', 'Person 1', 'Person 2', 'Person 1', 'Person 2',
'Person 2', 'Person 1', 'Person 2', 'Person 2', 'Person 1', 'Person 2', 'Person 2',
'Person 1', 'Person 2', 'Person 1', 'Person 2'],
'message': [
'Hello', 'Hi there', "How's things", 'good', 'I am glad', 'Me too.',
'Then we are both glad', 'Indeed we are.',
'I sure hope this is enough fake conversation for stackoverflow.',
'Better write a few more messages just in case',
"But the message content isn't relevant", 'Oh yeah.', "I'm going to stop now.",
'redacted', 'redacted', 'redacted', 'redacted', 'redacted', 'redacted', 'redacted',
'redacted', 'redacted', 'redacted', 'redacted', 'redacted', 'redacted', 'redacted',
'redacted', 'redacted', 'redacted', 'redacted', 'redacted']},
index = pd.DatetimeIndex([
pd.Timestamp('2019-07-29 19:58:00'), pd.Timestamp('2019-07-29 20:03:00'),
pd.Timestamp('2019-08-01 19:22:00'), pd.Timestamp('2019-08-01 19:23:00'),
pd.Timestamp('2019-08-01 19:25:00'), pd.Timestamp('2019-08-04 11:28:00'),
pd.Timestamp('2019-08-04 11:29:00'), pd.Timestamp('2019-08-04 11:29:00'),
pd.Timestamp('2019-08-04 12:43:00'), pd.Timestamp('2019-08-04 12:49:00'),
pd.Timestamp('2019-08-04 12:51:00'), pd.Timestamp('2019-08-04 12:51:00'),
pd.Timestamp('2019-08-25 22:33:00'), pd.Timestamp('2019-08-27 11:55:00'),
pd.Timestamp('2019-08-27 18:35:00'), pd.Timestamp('2019-11-06 18:53:00'),
pd.Timestamp('2019-11-06 18:54:00'), pd.Timestamp('2019-11-06 20:42:00'),
pd.Timestamp('2019-11-07 00:16:00'), pd.Timestamp('2019-11-07 15:24:00'),
pd.Timestamp('2019-11-07 16:06:00'), pd.Timestamp('2019-11-08 11:48:00'),
pd.Timestamp('2019-11-08 11:53:00'), pd.Timestamp('2019-11-08 11:55:00'),
pd.Timestamp('2019-11-08 11:55:00'), pd.Timestamp('2019-11-08 11:59:00'),
pd.Timestamp('2019-11-08 12:03:00'), pd.Timestamp('2019-12-24 13:40:00'),
pd.Timestamp('2019-12-24 13:42:00'), pd.Timestamp('2019-12-24 13:43:00'),
pd.Timestamp('2019-12-24 13:44:00'), pd.Timestamp('2019-12-24 13:44:00')]))
df_group = df.groupby(["sender"])
df_count = df_group[["sender"]].resample("D").count()
df_plot = pd.concat([df_count.loc['Person 1', :],
df_count.loc['Person 2', :]],
axis=1)
df_plot.columns = ['Sender 1', 'Sender 2']
df_plot.plot.area()
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