Line Chart

notebook setup:

  • Plot S&P 500 price on a line chart
    • S&P 500 (^GSPC): data/^GSPC.csv
  • color palette:
    • the palette is downloaded from adobe
    • colors

png

Hex RGBA HSLA
#8C4660 rgba(140,70,95, 1) hsla(337, 33, 41, 1)
#7988D9 rgba(121,135,216, 1) hsla(230, 55, 66, 1)
#252940 rgba(36,40,63, 1) hsla(230, 26, 19, 1)
#54628C rgba(84,98,140, 1) hsla(225, 25, 44, 1)
#F2AEAE rgba(242,174,174, 1) hsla(0, 72, 81, 1)

caution:

  • it will take a long time to plot data with more than 5k samples (i.e. rows)

reference:

  1. top 50 matplotlib visualizations the master plots python
  2. matplotlib
import pandas as pd
import numpy as np


import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns

print('pandas version: ', pd.__version__)
print('numpy version: ', np.__version__)
print('matplotlib version: ', mpl.__version__)
print('seaborn version: ', sns.__version__)

pandas version:  1.3.4
numpy version:  1.21.4
matplotlib version:  3.5.0
seaborn version:  0.11.2

df=pd.read_csv('data/^GSPC.csv', sep='|')

df.head(2)
Date Open High Low Close Volume Dividends Stock Splits
0 1950-01-03 16.66 16.66 16.66 16.66 1260000 0 0
1 1950-01-04 16.85 16.85 16.85 16.85 1890000 0 0 
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 18111 entries, 0 to 18110
Data columns (total 8 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   Date          18111 non-null  object 
 1   Open          18111 non-null  float64
 2   High          18111 non-null  float64
 3   Low           18111 non-null  float64
 4   Close         18111 non-null  float64
 5   Volume        18111 non-null  int64  
 6   Dividends     18111 non-null  int64  
 7   Stock Splits  18111 non-null  int64  
dtypes: float64(4), int64(3), object(1)
memory usage: 1.1+ MB

df=df.loc[df['Date']>='2010-01-01', ['Date', 'Close']].copy(deep=True)
df.shape

(3014, 2)

df.head(2)
Date Close
15097 2010-01-04 1132.98999
15098 2010-01-05 1136.52002 
df.reset_index(inplace=True)

the following illustrates how to plot a line chart with customized labels

png

# the main part
plt.figure(figsize=(16,10), dpi= 80)
plt.plot('Date', 'Close', data=df, color='#8C4660')


# pretty up the graph
xtick_location = df.index.tolist()[::230]
xtick_labels = [x[:7] for x in df['Date'].tolist()[::230]]
plt.xticks(ticks=xtick_location, labels=xtick_labels, rotation=0, fontsize=12, horizontalalignment='center', alpha=.7)
plt.yticks(fontsize=12, alpha=.7)
plt.title("S&P 500 Index (2010 - 2021)", fontsize=15, loc='left', y=1.02, x=-0.04)
plt.grid(axis='both', alpha=.3)

# remove borders
plt.gca().spines["top"].set_alpha(0.1)    
plt.gca().spines["bottom"].set_alpha(0.1)
plt.gca().spines["right"].set_alpha(0.1)    
plt.gca().spines["left"].set_alpha(0.1)    

plt.show()

png

the following illustrates how to add titles to x-axis and y-axis

png

# the main part
plt.figure(figsize=(16,10), dpi= 80)
plt.plot('Date', 'Close', data=df, color='#8C4660')


# pretty up the graph

#setup x-ticks
xtick_location = df.index.tolist()[::230]
xtick_labels = [x[:7] for x in df['Date'].tolist()[::230]]
plt.xticks(ticks=xtick_location, labels=xtick_labels, rotation=0, fontsize=12, horizontalalignment='center', alpha=.7)

#setup y-ticks
plt.ylim(0, 5500)
ytick_location = [1000, 2000, 3000, 4000, 5000, 5500]
ytick_labels = ['1,000', '2,000', '3,000', '4,000', '5,000', '___' ]
plt.yticks(ticks=ytick_location,labels=ytick_labels,  fontsize=12, alpha=.7)

#setup x-label, y-label, title
plt.xlabel('date', loc='center', fontsize=13, alpha=.7, y=-0.1)
plt.ylabel('price', rotation=0, fontsize=15, alpha=.7, y=0.98, x=0.1)
plt.title("S&P 500 Index (2010 - 2021)", fontsize=15, loc='left', y=1.03, x=-0.07)


# setup grid and borders
plt.grid(axis='both', alpha=.3)
plt.gca().spines["top"].set_alpha(0.1)    
plt.gca().spines["bottom"].set_alpha(0.1)
plt.gca().spines["right"].set_alpha(0.1)    
plt.gca().spines["left"].set_alpha(0.1)   

plt.show()

png