我正在使用的數(shù)據(jù)是對(duì)話消息日志。我有一個(gè)熊貓數(shù)據(jù)幀，以日期戳作為索引，還有兩列;一個(gè)用于“發(fā)件人”，一個(gè)用于“消息”。我只是試圖繪制一個(gè)隨時(shí)間變化的消息堆棧圖。我實(shí)際上并不需要消息的內(nèi)容，因此我按如下方式清理了數(shù)據(jù)：dfgrouped = df.groupby(["sender"])dfgrouped[["sender"]].resample("D").count()這將提供按對(duì)話中的每個(gè)發(fā)件人分組的數(shù)據(jù)幀，其中 DateTime 作為索引，并在給定日期發(fā)送的消息數(shù)。dfgrouped[["sender"]].get_group("Joe Bloggs").resample("D").count()...將給出一個(gè)只有一個(gè)用戶的數(shù)據(jù)幀，并且他們的消息每天計(jì)數(shù)。我想知道如何使用 matplotlib 來(lái)繪制一個(gè)堆棧圖，其中每個(gè)“發(fā)送方”是一條不同的線。我也無(wú)法通過(guò)ax.stackplot(dfgrouped[["sender"]].resample("D").count())或通過(guò)循環(huán)：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(xiàn)1963條經(jīng)驗(yàn) 獲得超6個(gè)贊
您可以使用熊貓自己的堆棧圖函數(shù)，df.plot.area（）。這是 Matplotlib 函數(shù)的包裝器，用作數(shù)據(jù)幀上的方法。您只需要將數(shù)據(jù)保持在正確的形狀。通過(guò)您的分組和計(jì)數(shù)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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