Plot datetime.date / time series in a pandas dataframe
Question:
I created a pandas dataframe from some value counts on particular calendar dates. Here is how I did it:
time_series = pd.DataFrame(df['Operation Date'].value_counts().reset_index())
time_series.columns = ['date', 'count']
Basically, it is two columns, the first "date" is a column with datetime.date objects and the second column, "count" are simply integer values. Now, I’d like to plot a scatter or a KDE to represent how the value changes over the calendar days.
But when I try:
time_series.plot(kind='kde')
plt.show()
I get a plot where the x-axis is from -50 to 150 as if it is parsing the datetime.date objects as integers somehow. Also, it is yielding two identical plots rather than just one.
Any idea how I can plot them and see the calendars day along the x-axis?
Answers:
you sure you got datetime? i just tried this and it worked fine:
df = date count
7 2012-06-11 16:51:32 1.0
3 2012-09-28 08:05:14 12.0
19 2012-10-01 18:01:47 4.0
2 2012-10-03 15:18:23 29.0
6 2012-12-22 19:50:43 4.0
1 2013-02-19 19:54:03 28.0
9 2013-02-28 16:08:40 17.0
12 2013-03-12 08:42:55 6.0
4 2013-04-04 05:27:27 6.0
17 2013-04-18 09:40:37 29.0
11 2013-05-17 16:34:51 22.0
5 2013-07-07 14:32:59 16.0
14 2013-10-22 06:56:29 13.0
13 2014-01-16 23:08:46 20.0
15 2014-02-25 00:49:26 10.0
18 2014-03-19 15:58:38 25.0
0 2014-03-31 05:53:28 16.0
16 2014-04-01 09:59:32 27.0
8 2014-04-27 12:07:41 17.0
10 2014-09-20 04:42:39 21.0
df = df.sort_values('date', ascending=True)
plt.plot(df['date'], df['count'])
plt.xticks(rotation='vertical')
EDIT:
if you want a scatter plot you can:
plt.plot(df['date'], df['count'], '*')
plt.xticks(rotation='vertical')
If the column is datetime dtype (not object), then you can call plot() directly on the dataframe. You don’t need to sort by date either, it’s done behind the scenes if x-axis is datetime.
df['date'] = pd.to_datetime(df['date'])
df.plot(x='date', y='count', kind='scatter', rot='vertical');
You can also pass many arguments to make the plot nicer (add titles, change figsize and fontsize, rotate ticklabels, set subplots axis etc.) See the docs for full list of possible arguments.
df.plot(x='date', y='count', kind='line', rot=45, legend=None,
title='Count across time', xlabel='', fontsize=10, figsize=(12,4));
You can even use another column to color scatter plots. In the example below, the months are used to assign color. Tip: To get the full list of possible colormaps, pass any gibberish string to colormap and the error message will show you the full list.
df.plot(x='date', y='count', kind='scatter', rot=90, c=df['date'].dt.month, colormap='tab20', sharex=False);
I created a pandas dataframe from some value counts on particular calendar dates. Here is how I did it:
time_series = pd.DataFrame(df['Operation Date'].value_counts().reset_index())
time_series.columns = ['date', 'count']
Basically, it is two columns, the first "date" is a column with datetime.date objects and the second column, "count" are simply integer values. Now, I’d like to plot a scatter or a KDE to represent how the value changes over the calendar days.
But when I try:
time_series.plot(kind='kde')
plt.show()
I get a plot where the x-axis is from -50 to 150 as if it is parsing the datetime.date objects as integers somehow. Also, it is yielding two identical plots rather than just one.
Any idea how I can plot them and see the calendars day along the x-axis?
you sure you got datetime? i just tried this and it worked fine:
df = date count
7 2012-06-11 16:51:32 1.0
3 2012-09-28 08:05:14 12.0
19 2012-10-01 18:01:47 4.0
2 2012-10-03 15:18:23 29.0
6 2012-12-22 19:50:43 4.0
1 2013-02-19 19:54:03 28.0
9 2013-02-28 16:08:40 17.0
12 2013-03-12 08:42:55 6.0
4 2013-04-04 05:27:27 6.0
17 2013-04-18 09:40:37 29.0
11 2013-05-17 16:34:51 22.0
5 2013-07-07 14:32:59 16.0
14 2013-10-22 06:56:29 13.0
13 2014-01-16 23:08:46 20.0
15 2014-02-25 00:49:26 10.0
18 2014-03-19 15:58:38 25.0
0 2014-03-31 05:53:28 16.0
16 2014-04-01 09:59:32 27.0
8 2014-04-27 12:07:41 17.0
10 2014-09-20 04:42:39 21.0
df = df.sort_values('date', ascending=True)
plt.plot(df['date'], df['count'])
plt.xticks(rotation='vertical')
EDIT:
if you want a scatter plot you can:
plt.plot(df['date'], df['count'], '*')
plt.xticks(rotation='vertical')
If the column is datetime dtype (not object), then you can call plot() directly on the dataframe. You don’t need to sort by date either, it’s done behind the scenes if x-axis is datetime.
df['date'] = pd.to_datetime(df['date'])
df.plot(x='date', y='count', kind='scatter', rot='vertical');
You can also pass many arguments to make the plot nicer (add titles, change figsize and fontsize, rotate ticklabels, set subplots axis etc.) See the docs for full list of possible arguments.
df.plot(x='date', y='count', kind='line', rot=45, legend=None,
title='Count across time', xlabel='', fontsize=10, figsize=(12,4));
You can even use another column to color scatter plots. In the example below, the months are used to assign color. Tip: To get the full list of possible colormaps, pass any gibberish string to colormap and the error message will show you the full list.
df.plot(x='date', y='count', kind='scatter', rot=90, c=df['date'].dt.month, colormap='tab20', sharex=False);