How to plot a variable dataframe
Question:
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.
Answers:
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.
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 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.
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.
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()