I have a dataframe with a variable number of stock prices. In other words, I have to be able to plot the entire Dataframe, because I may encounter 1 to 10 stocks prices.
The x axis are dates, the Y axis are Stock prices. Here is a sample of my Df:
df = pd.DataFrame(all_Assets)
df2 = df.transpose()
print(df2)
Close Close Close
Date
2018-12-12 00:00:00-05:00 40.802803 24.440001 104.500526
2018-12-13 00:00:00-05:00 41.249191 25.119333 104.854965
2018-12-14 00:00:00-05:00 39.929325 24.380667 101.578560
2018-12-17 00:00:00-05:00 39.557732 23.228001 98.570381
2018-12-18 00:00:00-05:00 40.071678 22.468666 99.605057
This is not working
fig = go.Figure(data=go.Scatter(df2, mode='lines'),)
I need to plot this entire dataframe on a single chart, with 3 different lines. But the code has to adapt automatically if there is a fourth stock, fifth stock e.g. By the way , I want it to be a Logarithmic plot.
There is a sample in the reference, so let's try to graph it in wide and long format with express and in wide and long format with the graph object. You can choose from these four types to do what you need.
express wide format
df.head()
date GOOG AAPL AMZN FB NFLX MSFT
0 2018-01-01 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
1 2018-01-08 1.018172 1.011943 1.061881 0.959968 1.053526 1.015988
2 2018-01-15 1.032008 1.019771 1.053240 0.970243 1.049860 1.020524
3 2018-01-22 1.066783 0.980057 1.140676 1.016858 1.307681 1.066561
4 2018-01-29 1.008773 0.917143 1.163374 1.018357 1.273537 1.040708
import plotly.express as px
df = px.data.stocks()
fig = px.line(df, x='date', y=df.columns[1:])
fig.show()
express long format
df_long = df.melt(id_vars='date', value_vars=df.columns[1:],var_name='ticker')
px.line(df_long, x='date', y='value', color='ticker')
graph_objects wide format
import plotly.graph_objects as go
fig = go.Figure()
for ticker in df.columns[1:]:
fig.add_trace(go.Scatter(x=df['date'], y=df[ticker], name=ticker))
fig.show()
graph_objects long format
fig = go.Figure()
for ticker in df_long.ticker.unique():
dff = df_long.query('ticker == #ticker')
fig.add_trace(go.Scatter(x=dff['date'], y=dff['value'], name=ticker))
fig.show()
I recommend you to use pandas.DataFrame.plot. A minimal working example for your case should be just
df2.plot()
. Then just play around with the plot() method and your df2 dataframe to get exactly the output you need.
Related
There is this boring dataframe with stock data I have:
date close MA100 buy sell
2022-02-14 324.95 320.12 0 0
2022-02-13 324.87 320.11 1 0
2022-02-12 327.20 321.50 0 0
2022-02-11 319.61 320.71 0 1
Then I am plotting the prices
import pandas as pd
import matplotlib.pyplot as plt
df = ...
df['close'].plot()
df['MA100'].plot()
plt.show()
So far so good...
Then I'd like to show a marker on the chart if there was buy (green) or sell (red) on that day.
It's just to highlight if there was a transaction on that day. The exact intraday price at which the trade happened is not important.
So the x/y-coordinates could be the date and the close if there is a 1 in column buy (sell).
I am not sure how to implement this.
Would I need a loop to iterate over all rows where buy = 1 (sell = 1) and then somehow add these matches to the plot (probably with annotate?)
I'd really appreciate it if someone could point me in the right direction!
You can query the data frame for sell/buy and scatter plot:
fig, ax = plt.subplots()
df.plot(x='date', y=['close', 'MA100'], ax=ax)
df.query("buy==1").plot.scatter(x='date', y='close', c='g', ax=ax)
df.query("sell==1").plot.scatter(x='date', y='close', c='r', ax=ax)
Output:
I'm having trouble getting the following code to display a bar chart properly. The plot has very thin lines which are not visible until you zoom in, but even then it's not clear. I've tried to control with the width option to plt.bar() but it's not doing anything (e.g. tried 0.1, 1, 365).
Any pointers on what I'm doing wrong would be appreciated.
Many thanks
import pandas as pd
import numpy as np
import matplotlib.pylab as plt
import matplotlib.dates as mdates
plt.close('all')
mydateparser2 = lambda x: pd.datetime.strptime(x, "%m/%d/%Y")
colnames2=['Date','Net sales', 'Cost of sales']
df2 = pd.read_csv(r'account-test.csv', parse_dates = ['Date'] , date_parser = mydateparser2, index_col='Date')
df2= df2.filter(items=colnames2)
df2 = df2.sort_values('Date')
print (df2.info())
print (df2)
fig = plt.figure()
plt.bar(df2.index.values, df2['Net sales'], color='red', label='Net sales' )
plt.ylim(500000,2800000)
plt.show()
plt.legend(loc=4)
Resulting output (to show data types)
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 15 entries, 2005-12-31 to 2019-12-31
Data columns (total 2 columns):
Net sales 15 non-null int64
Cost of sales 15 non-null int64
dtypes: int64(2)
memory usage: 360.0 bytes
None
Net sales Cost of sales
Date
2005-12-31 1161400 907200
2006-12-31 1193100 928300
2007-12-31 1171100 888100
2008-12-31 1324900 1035700
2009-12-31 1108300 859800
2010-12-31 1173600 891000
2011-12-31 1392400 1050300
2012-12-31 1578200 1171500
2013-12-31 1678200 1224200
2014-12-31 1855500 1346700
2015-12-31 1861200 1328400
2016-12-31 2004300 1439700
2017-12-31 1973300 1421500
2018-12-31 2189100 1608300
2019-12-31 2355700 1715300
Maybe you are trying to plot too many bars on a small plot. Try fig = plt.figure(figsize=(12,6) to have a bigger plot. You can also pass width=0.9 to your bar command:
fig, ax = plt.subplots(figsize=(12,6))
df.plot.bar(y='Net sales', width=0.9, ax=ax) # modify width to your liking
Output:
I've got two pandas series, one with a 7 day rolling mean for the entire year and another with monthly averages. I'm trying to plot them both on the same matplotlib figure, with the averages as a bar graph and the 7 day rolling mean as a line graph. Ideally, the line would be graph on top of the bar graph.
The issue I'm having is that, with my current code, the bar graph is showing up without the line graph, but when I try plotting the line graph first I get a ValueError: ordinal must be >= 1.
Here's what the series' look like:
These are first 15 values of the 7 day rolling mean series, it has a date and a value for the entire year:
date
2016-01-01 NaN
2016-01-03 NaN
2016-01-04 NaN
2016-01-05 NaN
2016-01-06 NaN
2016-01-07 NaN
2016-01-08 0.088473
2016-01-09 0.099122
2016-01-10 0.086265
2016-01-11 0.084836
2016-01-12 0.076741
2016-01-13 0.070670
2016-01-14 0.079731
2016-01-15 0.079187
2016-01-16 0.076395
This is the entire monthly average series:
dt_month
2016-01-01 0.498323
2016-02-01 0.497795
2016-03-01 0.726562
2016-04-01 1.000000
2016-05-01 0.986411
2016-06-01 0.899849
2016-07-01 0.219171
2016-08-01 0.511247
2016-09-01 0.371673
2016-10-01 0.000000
2016-11-01 0.972478
2016-12-01 0.326921
Here's the code I'm using to try and plot them:
ax = series_one.plot(kind="bar", figsize=(20,2))
series_two.plot(ax=ax)
plt.show()
Here's the graph that generates:
Any help is hugely appreciated! Also, advice on formatting this question and creating code to make two series for a minimum working example would be awesome.
Thanks!!
The problem is that pandas bar plots are categorical (Bars are at subsequent integer positions). Since in your case the two series have a different number of elements, plotting the line graph in categorical coordinates is not really an option. What remains is to plot the bar graph in numerical coordinates as well. This is not possible with pandas, but is the default behaviour with matplotlib.
Below I shift the monthly dates by 15 days to the middle of the month to have nicely centered bars.
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(42)
import pandas as pd
t1 = pd.date_range("2018-01-01", "2018-12-31", freq="D")
s1 = pd.Series(np.cumsum(np.random.randn(len(t1)))+14, index=t1)
s1[:6] = np.nan
t2 = pd.date_range("2018-01-01", "2018-12-31", freq="MS")
s2 = pd.Series(np.random.rand(len(t2))*15+5, index=t2)
# shift monthly data to middle of month
s2.index += pd.Timedelta('15 days')
fig, ax = plt.subplots()
ax.bar(s2.index, s2.values, width=14, alpha=0.3)
ax.plot(s1.index, s1.values)
plt.show()
The problem might be the two series' indices are of very different scales. You can use ax.twiny to plot them:
ax = series_one.plot(kind="bar", figsize=(20,2))
ax_tw = ax.twiny()
series_two.plot(ax=ax_tw)
plt.show()
Output:
I have a dataframe which is structured as:
Date ticker adj_close
0 2016-11-21 AAPL 111.730
1 2016-11-22 AAPL 111.800
2 2016-11-23 AAPL 111.230
3 2016-11-25 AAPL 111.790
4 2016-11-28 AAPL 111.570
...
8 2016-11-21 ACN 119.680
9 2016-11-22 ACN 119.480
10 2016-11-23 ACN 119.820
11 2016-11-25 ACN 120.740
...
How can I plot based on the ticker the adj_close versus Date?
Simple plot,
you can use:
df.plot(x='Date',y='adj_close')
Or you can set the index to be Date beforehand, then it's easy to plot the column you want:
df.set_index('Date', inplace=True)
df['adj_close'].plot()
If you want a chart with one series by ticker on it
You need to groupby before:
df.set_index('Date', inplace=True)
df.groupby('ticker')['adj_close'].plot(legend=True)
If you want a chart with individual subplots:
grouped = df.groupby('ticker')
ncols=2
nrows = int(np.ceil(grouped.ngroups/ncols))
fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(12,4), sharey=True)
for (key, ax) in zip(grouped.groups.keys(), axes.flatten()):
grouped.get_group(key).plot(ax=ax)
ax.legend()
plt.show()
Similar to Julien's answer above, I had success with the following:
fig, ax = plt.subplots(figsize=(10,4))
for key, grp in df.groupby(['ticker']):
ax.plot(grp['Date'], grp['adj_close'], label=key)
ax.legend()
plt.show()
This solution might be more relevant if you want more control in matlab.
Solution inspired by: https://stackoverflow.com/a/52526454/10521959
The question is How can I plot based on the ticker the adj_close versus Date?
This can be accomplished by reshaping the dataframe to a wide format with .pivot or .groupby, or by plotting the existing long form dataframe directly with seaborn.
In the following sample data, the 'Date' column has a datetime64[ns] Dtype.
Convert the Dtype with pandas.to_datetime if needed.
Tested in python 3.10, pandas 1.4.2, matplotlib 3.5.1, seaborn 0.11.2
Imports and Sample Data
import pandas as pd
import pandas_datareader as web # for sample data; this can be installed with conda if using Anaconda, otherwise pip
import seaborn as sns
import matplotlib.pyplot as plt
# sample stock data, where .iloc[:, [5, 6]] selects only the 'Adj Close' and 'tkr' column
tickers = ['aapl', 'acn']
df = pd.concat((web.DataReader(ticker, data_source='yahoo', start='2020-01-01', end='2022-06-21')
.assign(ticker=ticker) for ticker in tickers)).iloc[:, [5, 6]]
# display(df.head())
Date Adj Close ticker
0 2020-01-02 73.785904 aapl
1 2020-01-03 73.068573 aapl
2 2020-01-06 73.650795 aapl
3 2020-01-07 73.304420 aapl
4 2020-01-08 74.483604 aapl
# display(df.tail())
Date Adj Close ticker
1239 2022-06-14 275.119995 acn
1240 2022-06-15 281.190002 acn
1241 2022-06-16 270.899994 acn
1242 2022-06-17 275.380005 acn
1243 2022-06-21 282.730011 acn
pandas.DataFrame.pivot & pandas.DataFrame.plot
pandas plots with matplotlib as the default backend.
Reshaping the dataframe with pandas.DataFrame.pivot converts from long to wide form, and puts the dataframe into the correct format to plot.
.pivot does not aggregate data, so if there is more than 1 observation per index, per ticker, then use .pivot_table
Adding subplots=True will produce a figure with two subplots.
# reshape the long form data into a wide form
dfp = df.pivot(index='Date', columns='ticker', values='Adj Close')
# display(dfp.head())
ticker aapl acn
Date
2020-01-02 73.785904 203.171112
2020-01-03 73.068573 202.832764
2020-01-06 73.650795 201.508224
2020-01-07 73.304420 197.157654
2020-01-08 74.483604 197.544434
# plot
ax = dfp.plot(figsize=(11, 6))
Use seaborn, which accepts long form data, so reshaping the dataframe to a wide form isn't necessary.
seaborn is a high-level api for matplotlib
sns.lineplot: axes-level plot
fig, ax = plt.subplots(figsize=(11, 6))
sns.lineplot(data=df, x='Date', y='Adj Close', hue='ticker', ax=ax)
sns.relplot: figure-level plot
Adding row='ticker', or col='ticker', will generate a figure with two subplots.
g = sns.relplot(kind='line', data=df, x='Date', y='Adj Close', hue='ticker', aspect=1.75)
import pandas.io.data as web
import datetime
import matplotlib.pyplot as plt
start = datetime.datetime.strptime('2/10/2016', '%m/%d/%Y')
end = datetime.datetime.strptime('2/24/2016', '%m/%d/%Y')
f = web.DataReader(['GOOG','AAPL'], 'yahoo', start, end)
#print 'Volume'
wha = f[['Adj Close']] #pick out Adj Close
x=wha[0,:]
print x.shape
ax = f['Adj Close'].plot(grid=True, fontsize=10, rot=45.)
ax.set_ylabel('Adjusted Closing Price ($)')
plt.legend(loc='upper center', ncol=2, bbox_to_anchor=(0.5,1.1), shadow=True, fancybox=True, prop={'size':10})
#plt.show()
As you can see above, I'm trying to pick out numerical values of individual stock prices for data manipulation.
with
#print wha[1,:]
x=wha[0,:]
print x.shape
i could get it down to a 9x2 matrix where you have two columns for GOOG and AAPL and 9 prices each.
I tried
print type(x)
and see that it's
<class 'pandas.core.frame.DataFrame'>
and by means of
wha2=x.values.tolist()
i was able to pick out the stock prices.
Is there an easy way for me to now plot prices of one stock (AAPL alone for example) vs Dates ?
What more tractable for data manipulation than a Pandas dataframe?!?
>>> f['Adj Close'].iloc[:8, :2]
AAPL GOOG
Date
2016-02-10 94.269997 684.119995
2016-02-11 93.699997 683.109985
2016-02-12 93.989998 682.400024
2016-02-16 96.639999 691.000000
2016-02-17 98.120003 708.400024
2016-02-18 96.260002 697.349976
2016-02-19 96.040001 700.909973
2016-02-22 96.879997 706.460022
From your panel data, I first select the column Adj Close. I then used iloc for index based location filtering, selecting rows 0-8 and columns 0-1.
To just get adj close for Apple:
>>> f['Adj Close'].loc[:, 'AAPL']
Date
2016-02-10 94.269997
2016-02-11 93.699997
2016-02-12 93.989998
2016-02-16 96.639999
2016-02-17 98.120003
2016-02-18 96.260002
2016-02-19 96.040001
2016-02-22 96.879997
2016-02-23 94.690002
Name: AAPL, dtype: float64
Here is a link to indexing in the documentation.
http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-and-selecting-data
>>> f['Adj Close'].corr()
AAPL GOOG
AAPL 1.00000 0.87332
GOOG 0.87332 1.00000