amount
date
2014-01-06 1
2014-01-07 1
2014-01-08 4
2014-01-09 1
2014-01-14 1
我希望能够绘制一个散点图，以时间为 x 轴，以金额为 y 轴，在数据上绘制一条线来引导观看者的眼光。如果我使用 Pandas 绘图，例如 df.plot(style="o")，这样不太对，因为没有连线。我想要类似这里（链接）给出的例子。
注意：这与Ian Thompson的答案有很多共同之处，但是方法不同以至于需要单独回答。我使用问题中提供的DataFrame格式并避免更改索引。
Seaborn和其他库对于日期轴的处理可能并不如您所希望的那样好。以下是我解决此问题的方法：
Seaborn将比日期更好地处理这些内容。这是一个方便的技巧，可以在日期和不喜欢日期的库之间进行各种数学计算。
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!
由于Seaborn对日期的处理有些困难，我将创建一个解决方法。首先，我将把日期列设置为我的索引：
# Make dataframe
df = pd.DataFrame({'amount' : [1,
1,
4,
1,
1]},
index = ['2014-01-06',
'2014-01-07',
'2014-01-08',
'2014-01-09',
'2014-01-14'])
其次，将索引转换为pd.DatetimeIndex：
# Make index pd.DatetimeIndex
df.index = pd.DatetimeIndex(df.index)
用它替换原来的内容：
# Make new index
idx = pd.date_range(df.index.min(), df.index.max())
# Replace original index with idx
df = df.reindex(index = idx)
第四步，由于Seaborn不支持日期和回归线很好地兼容，因此我将创建一个行计数列，作为我们的x轴：
# Insert row count
df.insert(df.shape[1],
'row_count',
df.index.value_counts().sort_index().cumsum())
# Plot regression using Seaborn
fig = sns.regplot(data = df, x = 'row_count', y = 'amount')
# Change x-ticks to dates
labels = [item.get_text() for item in fig.get_xticklabels()]
# Set labels for 1:10 because labels has 11 elements (0 is the left edge, 11 is the right
# edge) but our data only has 9 elements
labels[1:10] = df.index.date
# Set x-tick labels
fig.set_xticklabels(labels)
# Rotate the labels so you can read them
plt.xticks(rotation = 45)
# Change x-axis title
plt.xlabel('date')
plt.show();
datetime dtype 值必须被转换成类似于 ordinal 的东西。sklearn.linear_model.LinearRegression 计算模型，然后用 matplotlib.pyplot.plot 添加回归线来完成。
sns.lineplot(x=[x1_date, x2_date], y=[y1, y2], label='线性模型', color='洋红色') 也可以实现。python 3.8.11、pandas 1.3.2、matplotlib 3.4.3、sklearn 0.24.2 中测试通过import yfinance as yf # for data
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
# download the data
data = yf.download('aapl', '2019-01-02', '2021-01-01')
# add an ordinal column because sklearn doesn't work with datetimes
data['ordinal'] = data.index.map(pd.Timestamp.toordinal)
# create the model
model = LinearRegression()
# extract x and y from dataframe data
x = data[['ordinal']]
y = data[['Adj Close']]
# fit the mode
model.fit(x, y)
# print the slope and intercept if desired
print('intercept:', model.intercept_[0])
print('slope:', model.coef_[0][0])
# select x1 and x2 and get the corresponding date from the index
x1 = data.ordinal.min()
x1_date = data[data.ordinal.eq(x1)].index[0]
x2 = data.ordinal.max()
x2_date = data[data.ordinal.eq(x2)].index[0]
# calculate y1, given x1
y1 = model.predict(np.array([[x1]]))[0][0]
print('y1:', y1)
# calculate y2, given x2
y2 = model.predict(np.array([[x2]]))[0][0]
print('y2:', y2)
[out]:
intercept: -90078.45713565295
slope: 0.12225139598567565
y1: 28.279040945126326
y2: 117.40030861868581
ax1 = data.plot(y='Adj Close', c='k', figsize=(15, 6), grid=True, legend=False)
ax1.plot([x1_date, x2_date], [y1, y2], label='Linear Model', c='magenta')
ax1.legend()
import datetime as dt，所以我不得不使用new_labels = [dt.date.fromordinal(int(item)) for item in ax.get_xticks()]我猜这个答案假设用户已经执行了from datetime import date。 - Tom Bush