我有一个大熊猫数据框,它是登录网站的用户ID的日志:
id datetime
130 2018-05-17 19:46:18
133 2018-05-17 20:59:57
133 2018-05-17 21:54:01
142 2018-05-17 22:49:07
114 2018-05-17 23:02:34
136 2018-05-18 06:06:48
136 2018-05-18 12:21:38
180 2018-05-18 12:49:33
.......
120 2018-05-18 14:03:58
120 2018-05-18 15:28:36
如何将上述熊猫数据框可视化为时间序列图?例如,我想将每个人id
的登录频率表示为不同颜色的线条(请注意,我大约有400 ids
)。像这样的情节(*)
:
]
我试图:
from datetime import date
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import pandas as pd
# set your data as df
# strip only YYYY-mm-dd part from original `datetime` column
df3.timestamp = df3.datetime.apply(lambda x: str(x)[:10])
df3.timestamp = df3.datetime.apply(lambda x: date(int(x[:4]), int(x[5:7]), int(x[8:10])))
# plot
plt.figure(figsize=(150,10))
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
plt.gca().xaxis.set_major_locator(mdates.DayLocator())
plt.plot(df3.datetime[:800], df3.id[:800], '-')
plt.gcf().autofmt_xdate()
和
import matplotlib.dates as dates
df5 = df3.set_index('datetime')
df5.plot(x_compat=True)
plt.gca().xaxis.set_major_locator(dates.DayLocator())
plt.gca().xaxis.set_major_formatter(dates.DateFormatter('%d\n\n%a'))
plt.gca().invert_xaxis()
plt.gcf().autofmt_xdate(rotation=0, ha="center")
plt.figure(figsize=(150,10))
但是,我得到了这样的东西:
]
关于如何获得类似于(*)
的情节的想法吗?
答案 0 :(得分:1)
我已经稍微处理了您的示例数据,以便一位用户登录三天。尝试中的问题是您试图“仅绘制”登录名。如果要查看登录频率,则必须进行计算。因此,我读取了数据并使用了正确的DateTime索引,然后使用groupby
后跟resample
来计算频率。我认为,如果有400位用户,这可能会有些混乱,但这将绘制出每位用户的每日登录信息。
import pandas
import io
d = """id,datetime
130,2018-05-17T19:46:18
133,2018-05-17T20:59:57
133,2018-05-17T21:54:01
142,2018-05-17T22:49:07
114,2018-05-17T23:02:34
136,2018-05-18T06:06:48
136,2018-05-18T12:21:38
130,2018-05-18T12:49:33
120,2018-05-18T14:03:58
130,2018-05-19T15:28:36"""
# for the data aboce, this is a quick way to parse it
df = pandas.read_csv(io.StringIO(d), parse_dates=['datetime'], index_col='datetime')
# This method is more roundabout but is perhaps useful if you have other data
df = pandas.read_csv(io.StringIO(d))
df.datetime = pandas.to_datetime(df.datetime)
df = df.set_index('datetime')
# Plot daily logins per user id
r = df.groupby('id').resample('D').apply(len).unstack('id').plot()