Pandas Dataframe Line Plot Display Date on Xaxis

Pandas Dataframe line plot display date on xaxis

In general the datetime utilities of pandas and matplotlib are incompatible. So trying to use a matplotlib.dates object on a date axis created with pandas will in most cases fail.

One reason is e.g. seen from the documentation

datetime objects are converted to floating point numbers which represent time in days since 0001-01-01 UTC, plus 1. For example, 0001-01-01, 06:00 is 1.25, not 0.25.

However, this is not the only difference and it is thus advisable not to mix pandas and matplotlib when it comes to datetime objects.

There is however the option to tell pandas not to use its own datetime format. In that case using the matplotlib.dates tickers is possible. This can be steered via.

df.plot(x_compat=True)

Since pandas does not provide sophisticated formatting capabilities for dates, one can use matplotlib for plotting and formatting.

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as dates

df = pd.DataFrame({'date':['20170527','20170526','20170525'],'ratio1':[1,0.98,0.97]})
df['date'] = pd.to_datetime(df['date'])

usePandas=True
#Either use pandas
if usePandas:
    df = df.set_index('date')
    df.plot(x_compat=True)
    plt.gca().xaxis.set_major_locator(dates.DayLocator())
    plt.gca().xaxis.set_major_formatter(dates.DateFormatter('%d\n\n%a'))
    plt.gca().invert_xaxis()
    plt.gcf().autofmt_xdate(rotation=0, ha="center")
# or use matplotlib
else:
    plt.plot(df["date"], df["ratio1"])
    plt.gca().xaxis.set_major_locator(dates.DayLocator())
    plt.gca().xaxis.set_major_formatter(dates.DateFormatter('%d\n\n%a'))
    plt.gca().invert_xaxis()

plt.show()

• Updated using the matplotlib object oriented API

usePandas=True
#Either use pandas
if usePandas:
    df = df.set_index('date')
    ax = df.plot(x_compat=True, figsize=(6, 4))
    ax.xaxis.set_major_locator(dates.DayLocator())
    ax.xaxis.set_major_formatter(dates.DateFormatter('%d\n\n%a'))
    ax.invert_xaxis()
    ax.get_figure().autofmt_xdate(rotation=0, ha="center")
    
# or use matplotlib
else:
    fig, ax = plt.subplots(figsize=(6, 4))
    ax.plot('date', 'ratio1', data=df)
    ax.xaxis.set_major_locator(dates.DayLocator())
    ax.xaxis.set_major_formatter(dates.DateFormatter('%d\n\n%a'))
    fig.invert_xaxis()

plt.show()

Pandas Plotting Display all date values on x-axis (matplolib only displays few values) formatted as MMM-YYYY

As mentioned in the comments, you have to set both, the Locator and the Formatter. This is explained well in the matplotlib documentation for graphs in general and separately for datetime axes. See also an explanation of the TickLocators. The formatting codes are derived from Python's strftime() and strptime() format codes.

from matplotlib import pyplot as plt
import pandas as pd
from matplotlib.dates import MonthLocator, DateFormatter

#fake data
import numpy as np
np.random.seed(123)
n = 100
df = pd.DataFrame({"Dates": pd.date_range("20180101", periods=n, freq="10d"), "A": np.random.randint(0, 100, size=n), "B": np.random.randint(0, 100, size=n),})
df.set_index("Dates", inplace=True)
print(df)

ax = df.plot()

#defines the tick location 
ax.xaxis.set_major_locator(MonthLocator())
#defines the label format
ax.xaxis.set_major_formatter(DateFormatter("%b-%y"))
ax.tick_params(axis="x", labelrotation= 90)

plt.tight_layout()
plt.show()

Sample output:

Pandas dataframe plot(): x-axis date labels display but not data

• Dataset is at Assets: Total Assets: Total Assets (Less Eliminations from Consolidation): Wednesday Level (WALCL)
• Verify the DATE column is in a datetime format by using parse_dates with .read_csv.

Set DATE as the index

import pandas as pd
import numpy as np

# verify the DATE column is in a datetime format and set it as the index
dfData = pd.read_csv('WALCL.csv', skiprows=0, parse_dates=['DATE'], index_col='DATE')

# plot the data
ax = dfData.plot(figsize=(20, 8))

datemin = np.datetime64(dfData.index.min(), 'Y')
datemax = np.datetime64(dfData.index.max(), 'Y') + np.timedelta64(1, 'Y')
ax.set_xlim(datemin, datemax)

Leave DATE as a column

import pandas as pd

# read file
dfData = pd.read_csv('WALCL.csv', skiprows=0, parse_dates=['DATE'])

# plot data
ax = dfData.plot('DATE', 'WALCL', figsize=(20, 8))

Pandas dataframe x-axis date incorrect formatting when plotted

I don't really know why, but if you juste don't convert the dates to datetime objects, it is working just fine :

I would say that matplotlib has already something to convert dates and doing it twice might ruin it, but that's purely a speculation.

Date in form of yyyy-mm in x axis for line plot pandas python

Construct some sample data and extract the sample Series data2 as in your question:

df = pd.DataFrame(
    data=np.random.rand(20, 3), 
    index=pd.date_range(start='2019-01-01', freq='MS', periods=20))
data2 = df.iloc[:, 2]

Assuming you plot your dataframe explicitly with matplotlib like this:

fig, ax = plt.subplots()
ax.plot(data2.index, data2)

You can set the ticks with the matplotlib.dates module:

import matplotlib as mpl

ax.xaxis.set_major_locator(mpl.dates.MonthLocator(interval=3))
ax.xaxis.set_major_formatter(mpl.dates.DateFormatter('%Y-%m'))

ax.tick_params(rotation=30, axis='x')  # rotate xticks

interval=3 selects every third month, the date formatter %Y-%m produces the YYYY-MM format.

More on date formatting can be found in the documentation of the matplotlib.dates module and specifically for locating months in matplotlib.dates.MonthLocator.

Plotting with the pandas plotting interface (f.i. df.plot()) may require other date formatting methods, since the conversion to axis-units is handled differently in pandas. Thus I'd always recommend sticking to explicitly using matplotlib when plotting with dates to get reproducible results.

Matplotlib and Dataframes: How do I put the Date on x-axis from the Date column in my dataframe?

You should set Dateas index.

Temps = Temps.set_index('Date')
Temps['High'].plot()
Temps['Low'].plot()

How to display Dates on x axis in the graph with DataFrame in pandas

The key is your source.index is as str type. Since your are working with stock prices, i.e. Time Series data, so the better way is to turn the index as datetime type, with the parameter: parse_dates=True:

>>> df = pd.read_csv('Walmart_FB_2014_2017.csv', index_col='Date', parse_dates=True)
>>> dff = df / df.iloc[0]
>>> dff.plot()

Also, for simple case plots, Pandas built-in visualization ability can just work.

Check the dtype difference:

>>> source = pd.read_csv('Walmart_FB_2014_2017.csv', index_col='Date')
>>> source.index
Index(['2013-12-31', '2014-01-02', '2014-01-03', '2014-01-06', '2014-01-07',
       ...
       '2017-04-03', '2017-04-04', '2017-04-05', '2017-04-06', '2017-04-07'],
      dtype='object', name='Date', length=824)
>>> # Note the differences.
>>> df = pd.read_csv('Walmart_FB_2014_2017.csv', index_col='Date', parse_dates=True)
>>> df.index
DatetimeIndex(['2013-12-31', '2014-01-02', '2014-01-03', '2014-01-06',
               ...
               '2017-04-04', '2017-04-05', '2017-04-06', '2017-04-07'],
              dtype='datetime64[ns]', name='Date', length=824, freq=None)

---

Related Topics