seaborn 2 line plot

 import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

sns.set(style="darkgrid")

from scipy import signal

x = np.linspace(0, 1, 500)

y = signal.sawtooth(2 * np.pi * 5 * x)

df = pd.DataFrame(dict(xLabel = x, yLabel = y))

sns.relplot(x="xLabel", y="yLabel", kind="line", sort=False, data=df)

magnitude = np.linspace(0, 100, 500)

angle = np.linspace(0, 50*np.pi, 500)

x = magnitude * np.sin(angle)

y = magnitude * np.cos(angle)

df = pd.DataFrame(dict(xLabel = x, yLabel = y))

sns.relplot(x="xLabel", y="yLabel", kind="line", sort=False, data=df)

fmri = sns.load_dataset("fmri")

fmri.tail(10)

subject timepoint event region signal

1054 s5 8 cue frontal -0.028292

1055 s4 8 cue frontal -0.160821

1056 s3 8 cue frontal -0.033848

1057 s2 8 cue frontal -0.069666

1058 s1 8 cue frontal -0.136059

1059 s0 8 cue frontal 0.018165

1060 s13 7 cue frontal -0.029130

1061 s12 7 cue frontal -0.004939

1062 s11 7 cue frontal -0.025367

1063 s0 0 cue parietal -0.006899

#mean and the 95% confidence interval around the mean:

sns.relplot(x="timepoint", y="signal", kind="line", data=fmri);

#mean only

sns.relplot(x="timepoint", y="signal", ci=None, kind="line", data=fmri);

#mean + standard deviation

sns.relplot(x="timepoint", y="signal", kind="line", ci="sd", data=fmri);

sns.relplot(x="timepoint", y="signal", hue="event", kind="line", data=fmri);

sns.relplot(x="timepoint", y="signal", hue="region", style="event",

            kind="line", data=fmri);

sns.relplot(x="timepoint", y="signal", hue="region", style="event",

            dashes=False, markers=True, kind="line", data=fmri);

from datetime import datetime

date = ["08/28/2019", "08/29/2019", "08/30/2019",...]

dateArray = [datetime.strptime(_, '%m/%d/%Y').date() for _ in date]

price = [23.889999389648438, 23.829999923706055, 21.959999084472656, ...]

df = pd.DataFrame(dict(date=dateArray, price=price))

df.tail()

date price

2549 2020-08-13 1621.000000

2550 2020-08-14 1650.709961

2551 2020-08-17 1835.640015

2552 2020-08-18 1887.089966

2553 2020-08-19 1878.530029

tsla = sns.relplot(x="date", y="price", kind="line", data=df)

tsla.fig.suptitle('TSLA')

fig, ax = plt.subplots()

# the size of A4 paper

fig.set_size_inches(11.7, 8.27)

plt.xticks(rotation=45)

plt.title('TSLA')

filter = (df['date'] > datetime.strptime("06/01/2019", '%m/%d/%Y').date())

tsla = sns.relplot(x="date", y="price", kind="line", data=df.loc[filter], ax=ax)

reference:

https://seaborn.pydata.org/tutorial/relational.html

string to date

https://www.journaldev.com/23365/python-string-to-datetime-strptime

add title

https://campus.datacamp.com/courses/introduction-to-data-visualization-with-seaborn/customizing-seaborn-plots-4?ex=7

rotate label

https://stackoverflow.com/questions/26540035/rotate-label-text-in-seaborn-factorplot

https://stackoverflow.com/questions/10998621/rotate-axis-text-in-python-matplotlib