How to groupby and plot aggregated data?

I have a column in my dataframe with dates of users logging in, it's formatted as follows:

0     2020-09-24 23:37:13+02:00
1     2020-09-24 22:08:02+02:00
2     2020-09-24 21:40:01+02:00
3     2020-09-24 21:16:22+02:00
4     2020-09-24 19:22:22+02:00
                 ...           
425   2020-09-07 12:55:56+02:00
426   2020-09-07 05:24:19+02:00
427   2020-09-07 05:23:17+02:00
428   2020-09-01 13:15:03+02:00
429   2020-09-01 13:10:24+02:00
Name: Login, Length: 430, dtype: datetime64[ns, Europe/Amsterdam]

I tried to visualize it properly, but each of my solutions has a problem. My first try has x-ticks of unequal length (1 day is the same length as 6 days).

df['Login'] = df['Login'].dt.strftime('%d/%m/%y')
login_time = [login for login, df in df.groupby("Login")]
fig = plt.figure(figsize=(10, 5))
plt.plot(login_time, df.groupby(["Login"]).count())
plt.xticks(login_time, rotation = "40", ha='right', fontsize=14)
plt.ylabel("Number of logins", fontsize=20)
plt.xlabel("Date", fontsize=22)
plt.show()

Changing df['Login'] = df['Login'].dt.strftime('%d/%m/%y') to df['Login'] = df['Login'].dt.date returns a plot which doesn't give value of 0 for login-less days.

I was thinking that maybe a bar chart would be working well with this data, but the problem is that changing plt.plot to plt.bar gives ValueError: shape mismatch: objects cannot be broadcast to a single shape, althought I was thinking that .count() should return integers, creating a 2D projection.

Unable to get around this problem myself, I'm asking you to help me out and show a python noob how it can be done better. Much appreciated!

Solution

Always configure a datetime column to a datetime type, in order to plot correctly.
- Plotting time series data as a str type, will lead the incorrect order, spacing, and other unexpected annoyances.
- df['Login'] = df['Login'].dt.strftime('%d/%m/%y') converts a datetime type to a str type.
A bar plot, with datetime data as the x-axis, has integer indexed tick locations (e.g. 0, 1, ..., n)
A line plot, with datetime data as the x-axis, has datetime tick locations.
Doing [login for login, df in df.groupby("Login")] and plt.plot(login_time, df.groupby(["Login"]).count()) is not necessary.
- Use .groupby and aggregate the desired metric, .count, and then plot the groupby object.

import pandas as pd
import matplotlib.pyplot as plt

# setup the dataframe
df = pd.DataFrame({'Login': ['2020-09-24 23:37:13+02:00', '2020-09-24 22:08:02+02:00', '2020-09-24 21:40:01+02:00', '2020-09-24 21:16:22+02:00', '2020-09-24 19:22:22+02:00 ', '2020-09-07 12:55:56+02:00', '2020-09-07 05:24:19+02:00', '2020-09-07 05:23:17+02:00', '2020-09-01 13:15:03+02:00', '2020-09-01 13:10:24+02:00']})

# convert to datetime type
df.Login = pd.to_datetime(df.Login, utc=True)

# groupby the date and count
dfg = df.groupby(df.Login.dt.date).count()

# plot the data as a barplot
ax = dfg.plot.bar(figsize=(8, 5), ylabel='Login Count', xlabel='Login Time')
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')

# plot a lineplot
ax = dfg.plot(marker='o', figsize=(8, 5), ylabel='Login Count', xlabel='Login Time')
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')

# plot a line plot on the bar plt
ax = dfg.plot.bar(figsize=(8, 5), ylabel='Login Count', xlabel='Login Time')
ax.plot(range(len(dfg.Login)), dfg.Login, color='k')
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')