How to Customise Pandas Date Time Stamp @ x-axis
date, pandas, plot, python, time
Solution
I have a solution to make the labels look consistent, though bear in mind that it will also include the time on the "larger scale" time plot.
The code below uses the `matplotlib.dates` functionality to choose a date format for the x-axis. Note that as we're using the matplotlib formatting you can't simple use `df.plot` but must instead use `plt.plot_date` and convert your index to the correct format.
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import dates
# Generate some random data and plot it
time = pd.date_range('07/11/2014', periods=1000, freq='5min')
ts = pd.Series(pd.np.random.randn(len(time)), index=time)
fig, ax = plt.subplots()
ax.plot_date(ts.index.to_pydatetime(), ts.data)
# Create your formatter object and change the xaxis formatting.
date_fmt = '%d/%m/%y %H:%M:%S'
formatter = dates.DateFormatter(date_fmt)
ax.xaxis.set_major_formatter(formatter)
plt.gcf().autofmt_xdate()
plt.show()
An example showing the fully zoomed out plot
An example showing the plot zoomed in.
Problem
When I plots the complete data works fine and displays the date on the x-axis: . When I zoom into particular portion to view: the plot shows only the time rather than date, I do understand with less points can't display different set of date but how to show date or set date format even if the graph is zoomed? ``` dataToPlot = pd.read_csv(fileName, names=['time','1','2','3','4','plotValue','6','7','8','9','10','11','12','13','14','15','16'], sep=',', index_col=0, parse_dates=True, dayfirst=True) dataToPlot.drop(dataToPlot.index[0]) startTime = dataToPlot.head(1).index[0] endTime = dataToPlot.tail(1).index[0] ax = pd.rolling_mean(dataToPlot_plot[startTime:endTime][['plotValue']],mar).plot(linestyle='-', linewidth=3, markersize=9, color='#FECB00') ``` Thanks in advance!