Values are plotted out of order with a datetime axis

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This is the code for showing the 'Close' prices for Amazon, and visualizing the data using matplotlib.pyplot in Python.

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('amzn_close.csv')
df = df.set_index(pd.DatetimeIndex(df['Date'].values))

plt.figure(figsize=(16,8))
plt.plot(df['Close'], label='Close')
plt.title('Close Price')
plt.xlabel('Date')
plt.ylabel('Price USD')
plt.show()

Unfortunately, it came out like this, with squiggly lines:

enter image description here

Can someone help me to present and visualize the data correctly?

amzn_close.csv

Date,Close
2021-03-05,3000.46
2021-04-01,3161.0
2021-03-17,3135.73
2021-02-23,3194.5
2021-03-10,3057.64
2021-03-16,3091.86
2021-03-18,3027.99
2021-02-25,3057.16
2021-04-15,3379.09
2021-03-22,3110.87
2021-04-14,3333.0
2021-03-25,3046.26
2021-03-24,3087.07
2021-02-26,3092.93
2021-04-20,3334.69
2021-04-19,3372.01
2021-04-16,3399.44
2021-04-08,3299.3
2021-03-08,2951.95
2021-03-30,3055.29
2021-03-02,3094.53
2021-03-09,3062.85
2021-02-24,3159.53
2021-02-22,3180.74
2021-04-22,3309.04
2021-03-01,3146.14
2021-03-15,3081.68
2021-03-26,3052.03
2021-04-05,3226.73
2021-03-31,3094.08
2021-03-03,3005.0
2021-04-23,3340.88
2021-04-26,3409.0
2021-03-19,3074.96
2021-03-23,3137.5
2021-04-21,3362.02
2021-03-29,3075.73
2021-04-12,3379.39
2021-04-07,3279.39
2021-04-13,3400.0
2021-04-27,3417.43
2021-04-06,3223.82
2021-03-12,3089.49
2021-03-11,3113.59
2021-03-04,2977.57
2021-04-09,3372.2
pandas seaborn matplotlib python
1 Answers
  • Two things to always verify are:
    1. The 'Date' should be set as a datetime64[ns] or DatetimeIndex dtype.
      • pd.to_datetime()
      • pd.DatetimeIndex()
      • Parse dates when importing data
    2. The value column, 'Close', should be a numeric dtype
    • Check the dtypes with df.info()

Use matplotlib directly

  • In this case, only the index needs to be sorted, because the y-axis is already numeric and the x-axis is already a datetime dtype.
  • The csv can also be read in with:
    • df = pd.read_csv('amzn_close.csv', parse_dates=['Date'], index_col=['Date'])
# load and format the data
df = pd.read_csv('amzn_close.csv')
df = df.set_index(pd.DatetimeIndex(df['Date'].values))

# sort the index
df.sort_index(inplace=True)

# plot
plt.figure(figsize=(16, 7))
plt.plot(df['Close'], label='Close')
plt.title('Close Price')
plt.xlabel('Date')
plt.ylabel('Price USD')
plt.show()

Use pandas.DataFrame.plot

  • Doesn't require sorting the index
# load the data
df = pd.read_csv('amzn_close.csv', parse_dates=['Date'], index_col=['Date'])

# plot the data
df.plot(figsize=(16, 7), title='Close Price', ylabel='Price USD', rot=0, legend=False)

Use seaborn.lineplot

  • Doesn't require sorting the index
# load the data
df = pd.read_csv('amzn_close.csv', parse_dates=['Date'], index_col=['Date'])

# plot
fig, ax = plt.subplots(figsize=(16, 7))
sns.lineplot(data=df, ax=ax, legend=False)
ax.set(title='Close Price', xlabel='Date', ylabel='Price USD')

Output for all implementations

enter image description here

Notes

  • Package versions:
    • pandas v1.2.4
    • seaborn v0.11.1
    • matplotlib v3.3.4
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