Plotting in a zooming in matplotlib subplot

Question

This question is from this tutorial found here: I want my plot to look like the one below but with time series data and the zoomed data not being x_lim , y_lim data but from a different source.

So in the plot above i would like the intraday data that is from a different source and the plot below would be daily data for some stock. But because they both have different source i cannot use a limit to zoom. For this i will be using yahoo datareader for daily and yfinance for intraday.

The code:

import pandas as pd
from pandas_datareader import data as web
from matplotlib.patches import ConnectionPatch

df = web.DataReader('goog', 'yahoo')
df.Close = pd.to_numeric(df['Close'], errors='coerce')

fig = plt.figure(figsize=(6, 5))
plt.subplots_adjust(bottom = 0., left = 0, top = 1., right = 1)
sub1 = fig.add_subplot(2,2,1)
sub1 = df.Close.plot()

sub2 = fig.add_subplot(2,1,2) # two rows, two columns, second cell
df.Close.pct_change().plot(ax =sub2)
sub2.plot(theta, y, color = 'orange')
con1 = ConnectionPatch(xyA=(df[1:2].index, df[2:3].Close), coordsA=sub1.transData, 
                       xyB=(df[4:5].index, df[5:6].Close), coordsB=sub2.transData, color = 'green')
fig.add_artist(con1)

I am having trouble with xy coordinates. With the code above i am getting :

TypeError: Cannot cast array data from dtype('O') to dtype('float64') according to the rule 'safe'

xyA=(df[1:2].index, df[2:3].Close)

What i had done here is that my xvalue is the date df[1:2].index and my y value is the price df[2:3].Close

Is converting the df to an array and then ploting my only option here? If there is any other way to get the ConnectionPatch to work kindly please advise.

---

df.dtypes

High         float64
Low          float64
Open         float64
Close        float64
Volume         int64
Adj Close    float64
dtype: object

Answer 1

The way matplotlib dates are plotted are by converting dates to floats as a number of days, starting with 0 on 1970-1-1, i.e. the POSIX timestamp zero. It’s different from that timestamp as it’s not the same resolution, i.e. “1” is a day instead of a second.

There’s 3 ways to compute that number,

• either use matplotlib.dates.date2num
• or use .toordinal() which gives you the right resolution and remove the offset corresponding to 1970-1-1,
• or get the POSIX timestamp and divide by the number of seconds in a day:

df['Close'] = pd.to_numeric(df['Close'], errors='coerce')
df['Change'] = df['Close'].pct_change()

con1 = ConnectionPatch(xyA=(df.index[0].toordinal() - pd.Timestamp(0).toordinal(), df['Close'].iloc[0]), coordsA=sub1.transData,
                       xyB=(df.index[1].toordinal() - pd.Timestamp(0).toordinal(), df['Change'].iloc[1]), coordsB=sub2.transData, color='green')
fig.add_artist(con1)

con2 = ConnectionPatch(xyA=(df.index[-1].timestamp() / 86_400, df['Close'].iloc[-1]), coordsA=sub1.transData,
                       xyB=(df.index[-1].timestamp() / 86_400, df['Change'].iloc[-1]), coordsB=sub2.transData, color='green')
fig.add_artist(con2)

You also need to make sure that you’re using values that are in range for the targeted axes, in your example you use Close values on sub2 which contains pct_change’d values.

Of course if you want the bottom of the boxes as in your example it’s easier to express the coordinates using the axes transform instead of the data transform:

from matplotlib.dates import date2num

con1 = ConnectionPatch(xyA=(0, 0), coordsA=sub1.transAxes,
                       xyB=(date2num(df.index[1]), df['Change'].iloc[1]), coordsB=sub2.transData, color='green')
fig.add_artist(con1)

con2 = ConnectionPatch(xyA=(1, 0), coordsA=sub1.transAxes,
                       xyB=(date2num(df.index[-1]), df['Change'].iloc[-1]), coordsB=sub2.transData, color='green')
fig.add_artist(con2)

To plot your candlesticks, I’d recommend using the mplfinance (previously matplotlib.finance) package:

import mplfinance as mpf
sub3 = fig.add_subplot(2, 2, 2)
mpf.plot(df.iloc[30:70], type='candle', ax=sub3)

---

Putting all this together in a single script, it could look like this:

import pandas as pd, mplfinance as mpf, matplotlib.pyplot as plt
from pandas_datareader import data as web
from matplotlib.patches import ConnectionPatch
from matplotlib.dates import date2num, ConciseDateFormatter, AutoDateLocator
from matplotlib.ticker import PercentFormatter

# Get / compute data
df = web.DataReader('goog', 'yahoo')
df['Close'] = pd.to_numeric(df['Close'], errors='coerce')
df['Change'] = df['Close'].pct_change()

# Pick zoom range
zoom_start = df.index[30]
zoom_end = df.index[30 + 8 * 5] # 8 weeks ~ 2 months

# Create figures / axes
fig = plt.figure(figsize=(18, 12))
top_left = fig.add_subplot(2, 2, 1)
top_right = fig.add_subplot(2, 2, 2)
bottom = fig.add_subplot(2, 1, 2)
fig.subplots_adjust(hspace=.35)

# Plot all 3 data
df['Close'].plot(ax=bottom, linewidth=1, rot=0, title='Daily closing value', color='purple')
bottom.set_ylim(0)

df.loc[zoom_start:zoom_end, 'Change'].plot(ax=top_left, linewidth=1, rot=0, title='Daily Change, zoomed')
top_left.yaxis.set_major_formatter(PercentFormatter())

# Here instead of df.loc[...] use your intra-day data
mpf.plot(df.loc[zoom_start:zoom_end], type='candle', ax=top_right, xrotation=0, show_nontrading=True)
top_right.set_title('Last day OHLC')

# Put ConciseDateFormatters on all x-axes for fancy date display
for ax in fig.axes:
    locator = AutoDateLocator()
    ax.xaxis.set_major_locator(locator)
    ax.xaxis.set_major_formatter(ConciseDateFormatter(locator))

# Add the connection patches
fig.add_artist(ConnectionPatch(
    xyA=(0, 0), coordsA=top_left.transAxes,
    xyB=(date2num(zoom_start), df.loc[zoom_start, 'Close']), coordsB=bottom.transData,
    color='green'
))

fig.add_artist(ConnectionPatch(
    xyA=(1, 0), coordsA=top_left.transAxes,
    xyB=(date2num(zoom_end), df.loc[zoom_end, 'Close']), coordsB=bottom.transData,
    color='green'
))

plt.show()