-
pandas.DataFrame.plot https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html uses
matplotlib
as the default plotting backend, but it encodes date ticks as unix timestamps, which results in OverflowError: int too big to convert
.
- 这里默认的是
kind='line'
, but marker='x', lw=0, ms=0.2
在OP中用来制作一个hacky散点图。
-
pandas.DataFrame.plot.scatter https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.scatter.html将正常工作。
- Using matplotlib.pyplot.scatter https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html will work as expected.
-
matplotlib: Date tick labels https://matplotlib.org/stable/gallery/text_labels_and_annotations/date.html
- Matplotlib 日期绘图是通过将日期实例转换为自纪元以来的天数(默认为 1970-01-01T00:00:00)来完成的
-
seaborn.scatterplot https://seaborn.pydata.org/generated/seaborn.scatterplot.html will also work:
sns.scatterplot(x=df.index, y=df.A, color='red', marker='x', ax=ax)
- 测试于
python 3.8.11
, pandas 1.3.2
, matplotlib 3.4.3
, seaborn 0.11.2
matplotlib.pyplot.scatter
- 额外的格式具有删除月份的效果(
'01'
)将在刻度标签中的时间之前(例如'%m %H:%M'
).
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(25, 6))
ax.scatter(x=df.index, y=df.A, color='red', marker='x')
hourlocator = mdates.HourLocator(interval=1) # adds some extra formatting, but not required
majorFmt = mdates.DateFormatter('%H:%M') # adds some extra formatting, but not required
ax.xaxis.set_major_locator(mdates.MinuteLocator())
ax.xaxis.set_major_formatter(majorFmt) # adds some extra formatting, but not required
_ = plt.xticks(rotation=90)
pandas.DataFrame.plot.scatter
- Also
pandas.DataFrame.plot
with kind='scatter'
ax = df.reset_index().plot(kind='scatter', x='Time', y='A', color='red', marker='x', figsize=(25, 6), rot=90)
# reset the index so Time will be a column to assign to x
ax = df.reset_index().plot.scatter(x='Time', y='A', color='red', marker='x', figsize=(25, 6), rot=90)
ax.xaxis.set_major_locator(mdates.MinuteLocator())
pandas.DataFrame.plot
xticks
ax = df.plot(color='red', marker='x', lw=0, ms=0.2, figsize=(25, 6))
# extract the xticks to see the format
ticks = ax.get_xticks()
print(ticks)
[out]:
array([1627801627, 1627803672], dtype=int64)
# convert the column to unix format to compare
(df.index - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s')
[out]:
Int64Index([1627801627, 1627801628, 1627801629, 1627801630, 1627801631,
1627801632, 1627801633, 1627801634, 1627801635, 1627801636,
...
1627803663, 1627803664, 1627803665, 1627803666, 1627803667,
1627803668, 1627803669, 1627803670, 1627803671, 1627803672],
dtype='int64', name='Time', length=2046)
matplotlib.pyplot.scatter
xticks
fig, ax = plt.subplots(figsize=(25, 6))
ax.scatter(x=df.index, y=df.A, color='red', marker='x')
ticks2 = ax.get_xticks()
print(ticks2)
[out]:
array([18840.29861111, 18840.30208333, 18840.30555556, 18840.30902778,
18840.3125 , 18840.31597222, 18840.31944444])