Use index in pandas to plot data

I have a pandas-Dataframe and use resample() to calculate means (e.g. daily or monthly means).
Here is a small example.

import pandas as pd  
import numpy as np

dates = pd.date_range('1/1/2000', periods=100)
df = pd.DataFrame(np.random.randn(100, 1), index=dates, columns=['A'])

                   A
2000-01-01 -1.210683
2000-01-02  2.242549
2000-01-03  0.801811
2000-01-04  2.353149
2000-01-05  0.359541

monthly_mean = df.resample('M').mean()

                   A
2000-01-31 -0.048088
2000-02-29 -0.094143
2000-03-31  0.126364
2000-04-30 -0.413753

How do I plot the monthly_mean now?

How do I manage to use the index of my new created DataFrame monthly_mean as the x-axis?

Answers:

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Method 1

Try this,

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

Method 2

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.

Method 3

Also,

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

Method 4

monthly_mean.plot(y='A')

Uses index as x-axis by default.

Method 5

• 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()

Method 6

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()

All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0