Matplotlib: y-axis normalised

Question

I have the following dataset

Date              Type        Label
2020-03-20         A            1
2020-03-20         A            0
2020-03-19         B            1
2020-03-17         A            1
2020-03-15         C            0
2020-03-19         A            0
2020-03-20         D            1
2020-03-20         A            1

that I would like to plot with normalised values in a multiple lines plot. The code below plots the different lines through time

import matplotlib.pyplot as plt

fig, ax = plt.subplots(1, figsize=[10,6])

(df.loc[df.Label.eq(1),].groupby(["Date","Type"]).agg({"Type":"count"})
 .unstack(1).droplevel(0,axis=1)
 .fillna(method="ffill")
 .plot(ax=ax, kind="line")
)

but when I try to apply normalisation

column_norm=['Type']
df[column_norm] = df[column_norm].apply(lambda x: (x - x.min()) / (x.max() - x.min()))

it fails, returning an error:

TypeError: unsupported operand type(s) for -: 'str' and 'str'

when I calculate min and max.

Can you please tell me how to get a plot with y axis normalised to 1?

Answer 1

Based on the small sample of data and the way that you are using count and fillna in the code you have shared, I figure that you are wanting to compute the normalized/rescaled cumulative sum of the count labels equal to one through time. Here is a step-by-step example of how to do this using a larger sample dataset:

import numpy as np   # v 1.19.2
import pandas as pd  # v 1.1.3

# Create sample dataset
rng = np.random.default_rng(seed=1)  # random number generator
dti = pd.date_range('2020-01-01', '2020-01-31', freq='D')
size = 2*dti.size
dfraw = pd.DataFrame(data=dict(Type = rng.choice(list('ABCD'), size=size),
                               Label = rng.choice([0,1], size=size),
                               Date = rng.choice(dti, size=size)))
dfraw.head()

You can simplify the reshaping of the dataframe by using the pivot_table method. Notice how the df.Label.eq(1) mask and the aggregation function count are replaced here by aggfunc='sum' which takes advantage of the fact that Label is numeric:

dfp = dfraw.pivot_table(values='Label', index='Date', columns='Type', aggfunc='sum')
dfp.head()

Then the normalized/rescaled cumulative sum can be computed for each variable using the apply method:

dfcs = dfp.apply(lambda x: x.cumsum()/x.sum(), axis=0)
dfcs.head()

Finally, the NaN values can be filled to make the lines in the plot continuous:

df = dfcs.fillna(method='ffill').fillna(value=0)
df.head()

ax = df.plot(figsize=(10,6))

# Format the tick labels using the default tick locations and format legend
ticks = ax.get_xticks()
ticklabels = pd.to_datetime(ticks, unit='D').strftime('%d-%b')
ax.set_xticks(ticks)
ax.set_xticklabels(ticklabels, rotation=0, ha='center')
ax.legend(title='Type', frameon=False);