我的 DataFrame 对象看起来像
数量日期2014-01-06 12014-01-07 12014-01-08 42014-01-09 12014-01-14 1
我想要一种散点图,时间沿 x 轴,数量在 y 上,用一条线穿过数据来引导观察者的视线.如果我使用 pandas plot df.plot(style="o")
这不太正确,因为该行不存在.我想要
ax = seaborn.regplot(数据=df,x='date_ordinal',y='金额',)# 收紧坐标轴以保持美观ax.set_xlim(df['date_ordinal'].min() - 1, df['date_ordinal'].max() + 1)ax.set_ylim(0, df['amount'].max() + 1)
ax.set_xlabel('date')new_labels = [date.fromordinal(int(item)) for item in ax.get_xticks()]ax.set_xticklabels(new_labels)
哒哒!
My DataFrame object looks like
amount
date
2014-01-06 1
2014-01-07 1
2014-01-08 4
2014-01-09 1
2014-01-14 1
I would like a sort of scatter plot with time along the x-axis, and amount on the y, with a line through the data to guide the viewer's eye. If I use the pandas plot df.plot(style="o")
it's not quite right, because the line is not there. I would like something like the examples here.
note: this has a lot in common with Ian Thompson's answer but the approach is different enough to have it be a separate answer. I use the DataFrame format provided in the question and avoid changing the index.
Seaborn and other libraries don't deal as well with datetime axes as you might like them to. Here's how I'd work around it:
Seaborn will deal better with these than with dates. This is a handy trick for doing all kind of mathy things with dates and libraries that don't love dates.
from datetime import date
df['date_ordinal'] = pd.to_datetime(df['date']).apply(lambda date: date.toordinal())
ax = seaborn.regplot(
data=df,
x='date_ordinal',
y='amount',
)
# Tighten up the axes for prettiness
ax.set_xlim(df['date_ordinal'].min() - 1, df['date_ordinal'].max() + 1)
ax.set_ylim(0, df['amount'].max() + 1)
ax.set_xlabel('date')
new_labels = [date.fromordinal(int(item)) for item in ax.get_xticks()]
ax.set_xticklabels(new_labels)
ta-daa!
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