使用大熊猫中的索引来绘制数据

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最佳答案

You can use reset_index to turn the index back into a column:

monthly_mean.reset_index().plot(x='index', y='A')

Look at monthly_mean.reset_index() by itself- the date is no longer in the index, but is a column in the dataframe, which is now just indexed by integers. If you look at the documentation for reset_index, you can get a bit more control over the process, including assigning sensible names to the index.

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Try this,

monthly_mean.plot(y='A', use_index=True)

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Also,

monthly_mean.plot(x=df.index, y='A')

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monthly_mean.plot(y='A')

Uses index as x-axis by default.

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When plotting line plots against the index, the simplest answer is to not assign any x or y.
This will plot lines for all numeric or datetime columns, without specifying y.

monthly_mean.plot()

Only specify y= if there are multiple columns and you want certain columns plotted.
Or select the columns before plotting (e.g. monthly_mean[[c1, c2, c5]].plot()).

# sample data with multiple columns (5 x 5)
df = pd.DataFrame(np.random.random_sample((5, 5)))


# method 1: specify y
df.plot(y=[0, 2, 4])


# method 2: select columns first
df[[0, 2, 4]].plot()

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Something like this, perhaps.

import requests
import pandas as pd
from pandas import DataFrame
import matplotlib.pyplot as plt
import seaborn as sns


# Intitialise data of lists
data = [{'Month': '2020-01-01', 'Expense':1000, 'ID':'123'},
{'Month': '2020-02-01', 'Expense':3000, 'ID':'123'},
{'Month': '2020-03-01', 'Expense':2000, 'ID':'123'},
{'Month': '2020-01-01', 'Expense':3000, 'ID':'456'},
{'Month': '2020-02-01', 'Expense':5000, 'ID':'456'},
{'Month': '2020-03-01', 'Expense':10000, 'ID':'456'},
{'Month': '2020-03-01', 'Expense':5000, 'ID':'789'},
{'Month': '2020-04-01', 'Expense':2000, 'ID':'789'},
{'Month': '2020-05-01', 'Expense':3000, 'ID':'789'}]
df = pd.DataFrame(data)
df

Then...

uniques = df['ID'].unique()


for i in uniques:
fig, ax = plt.subplots()
fig.set_size_inches(4,3)
df_single = df[df['ID']==i]
sns.lineplot(data=df_single, x='Month', y='Expense')
ax.set(xlabel='Time', ylabel='Total Expense')
plt.xticks(rotation=45)
plt.show()