Plotly: How to add trendline to a bar chart?

I am trying to add trendline to bar plot which is plotted by plotly

Code:

import plotly.express as px

fig = px.bar(count, x="date", y="count",trendline="ols")

fig.update_layout(
    xaxis_title="Date",
    yaxis_title = "Count"
)

fig.show()

Error:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-129-8b01de219d3c> in <module>
----> 1 fig = px.bar(count, x="date", y="count",trendline="ols")
      2 
      3 fig.update_layout(
      4     xaxis_title="Date",
      5     yaxis_title = "Count"

TypeError: bar() got an unexpected keyword argument 'trendline'

Here is the data

How can I add a trendline successfully to this plot?

Answer

px.bar has no trendline method. Since you’re trying trendline="ols" I’m guessing you’d like to create a linear trendline. And looking at your data, a linear trendline might just not be the best description of your dataset:

So you’ll have to add a trendline yourself. You can still have your bar chart using go.Bar, but maybe consider displaying the trendline as a line and not more bars.

A closer look into scikit or statsmodels should be well worth your while regarding non-linear trends. One simple approach is to estimate a log-linear trend after a recoding of your dataset. You’ll see that this ‘captures’ the exponential increase of your variable better than a simple linear trend does:

But is that good enough? I’ll let that decision be up to you. And as I’ve said, you should take a closer look at the linked resources.

Code for plot 1:

from sklearn.linear_model import LinearRegression
import plotly.graph_objects as go
import pandas as pd
import numpy as np
import datetime

# data
df=pd.DataFrame({'date': {0: '12.10.2019',
                  1: '13.10.2019',
                  2: '14.10.2019',
                  3: '15.10.2019',
                  4: '16.10.2019',
                  5: '17.10.2019',
                  6: '18.10.2019',
                  7: '19.10.2019',
                  8: '20.10.2019',
                  9: '21.10.2019',
                  10: '22.10.2019',
                  11: '23.10.2019',
                  12: '24.10.2019',
                  13: '25.10.2019',
                  14: '26.10.2019',
                  15: '27.10.2019',
                  16: '28.10.2019',
                  17: '29.10.2019',
                  18: '30.10.2019',
                  19: '31.10.2019',
                  20: '01.11.2019',
                  21: '02.11.2019',
                  22: '03.11.2019',
                  23: '04.11.2019',
                  24: '05.11.2019',
                  25: '06.11.2019',
                  26: '07.11.2019',
                  27: '08.11.2019',
                  28: '09.11.2019',
                  29: '10.11.2019',
                  30: '11.11.2019',
                  31: '12.11.2019',
                  32: '13.11.2019',
                  33: '14.11.2019',
                  34: '15.11.2019',
                  35: '16.11.2019',
                  36: '17.11.2019',
                  37: '18.11.2019',
                  38: '19.11.2019',
                  39: '20.11.2019',
                  40: '21.11.2019',
                  41: '22.11.2019',
                  42: '23.11.2019',
                  43: '24.11.2019',
                  44: '25.11.2019',
                  45: '26.11.2019',
                  46: '27.11.2019',
                  47: '28.11.2019',
                  48: '29.11.2019',
                  49: '30.11.2019',
                  50: '01.12.2019',
                  51: '02.12.2019',
                  52: '03.12.2019',
                  53: '04.12.2019',
                  54: '05.12.2019',
                  55: '06.12.2019',
                  56: '07.12.2019',
                  57: '08.12.2019',
                  58: '09.12.2019',
                  59: '10.12.2019',
                  60: '11.12.2019',
                  61: '12.12.2019',
                  62: '13.12.2019',
                  63: '14.12.2019',
                  64: '15.12.2019',
                  65: '16.12.2019',
                  66: '17.12.2019',
                  67: '18.12.2019',
                  68: '19.12.2019',
                  69: '20.12.2019',
                  70: '21.12.2019',
                  71: '22.12.2019',
                  72: '23.12.2019',
                  73: '24.12.2019',
                  74: '25.12.2019',
                  75: '26.12.2019',
                  76: '27.12.2019',
                  77: '28.12.2019',
                  78: '29.12.2019',
                  79: '30.12.2019',
                  80: '31.12.2019',
                  81: '01.01.2020',
                  82: '02.01.2020',
                  83: '03.01.2020',
                  84: '04.01.2020',
                  85: '05.01.2020',
                  86: '06.01.2020',
                  87: '07.01.2020',
                  88: '08.01.2020',
                  89: '09.01.2020',
                  90: '10.01.2020',
                  91: '11.01.2020',
                  92: '12.01.2020',
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                  94: '14.01.2020',
                  95: '15.01.2020',
                  96: '16.01.2020',
                  97: '17.01.2020',
                  98: '18.01.2020',
                  99: '19.01.2020',
                  100: '20.01.2020',
                  101: '21.01.2020',
                  102: '22.01.2020',
                  103: '23.01.2020',
                  104: '24.01.2020',
                  105: '25.01.2020',
                  106: '26.01.2020',
                  107: '27.01.2020',
                  108: '28.01.2020',
                  109: '29.01.2020',
                  110: '30.01.2020',
                  111: '31.01.2020'},
                 'count': {0: 19,
                  1: 12,
                  2: 13,
                  3: 18,
                  4: 13,
                  5: 19,
                  6: 15,
                  7: 14,
                  8: 12,
                  9: 6,
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                  12: 12,
                  13: 17,
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                  23: 13,
                  24: 14,
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                  26: 16,
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                  28: 17,
                  29: 13,
                  30: 14,
                  31: 14,
                  32: 12,
                  33: 6,
                  34: 14,
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                  36: 16,
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                  38: 19,
                  39: 18,
                  40: 17,
                  41: 17,
                  42: 17,
                  43: 17,
                  44: 19,
                  45: 15,
                  46: 20,
                  47: 21,
                  48: 19,
                  49: 18,
                  50: 22,
                  51: 21,
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                  53: 18,
                  54: 21,
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                  59: 28,
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                  81: 45,
                  82: 43,
                  83: 44,
                  84: 49,
                  85: 47,
                  86: 54,
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                  88: 57,
                  89: 62,
                  90: 65,
                  91: 55,
                  92: 67,
                  93: 69,
                  94: 72,
                  95: 45,
                  96: 89,
                  97: 87,
                  98: 90,
                  99: 121,
                  100: 140,
                  101: 173,
                  102: 163,
                  103: 171,
                  104: 183,
                  105: 165,
                  106: 189,
                  107: 201,
                  108: 230,
                  109: 290,
                  110: 311,
                  111: 321}})
Y=df['count']
X=df.index

# regression
reg = LinearRegression().fit(np.vstack(X), Y)
df['bestfit'] = reg.predict(np.vstack(X))

# plotly figure setup
fig=go.Figure()
fig.add_trace(go.Bar(name='X vs Y', x=X, y=Y.values))
fig.add_trace(go.Scatter(name='line of best fit', x=X, y=df['bestfit'], mode='lines'))

# plotly figure layout
fig.update_layout(xaxis_title = 'X', yaxis_title = 'Y')

fig.show()

Code for plot 2:

from sklearn.linear_model import LinearRegression
import plotly.graph_objects as go
import pandas as pd
import numpy as np
import datetime

# data
df=pd.DataFrame({'date': {0: '12.10.2019',
                  1: '13.10.2019',
                  2: '14.10.2019',
                  3: '15.10.2019',
                  4: '16.10.2019',
                  5: '17.10.2019',
                  6: '18.10.2019',
                  7: '19.10.2019',
                  8: '20.10.2019',
                  9: '21.10.2019',
                  10: '22.10.2019',
                  11: '23.10.2019',
                  12: '24.10.2019',
                  13: '25.10.2019',
                  14: '26.10.2019',
                  15: '27.10.2019',
                  16: '28.10.2019',
                  17: '29.10.2019',
                  18: '30.10.2019',
                  19: '31.10.2019',
                  20: '01.11.2019',
                  21: '02.11.2019',
                  22: '03.11.2019',
                  23: '04.11.2019',
                  24: '05.11.2019',
                  25: '06.11.2019',
                  26: '07.11.2019',
                  27: '08.11.2019',
                  28: '09.11.2019',
                  29: '10.11.2019',
                  30: '11.11.2019',
                  31: '12.11.2019',
                  32: '13.11.2019',
                  33: '14.11.2019',
                  34: '15.11.2019',
                  35: '16.11.2019',
                  36: '17.11.2019',
                  37: '18.11.2019',
                  38: '19.11.2019',
                  39: '20.11.2019',
                  40: '21.11.2019',
                  41: '22.11.2019',
                  42: '23.11.2019',
                  43: '24.11.2019',
                  44: '25.11.2019',
                  45: '26.11.2019',
                  46: '27.11.2019',
                  47: '28.11.2019',
                  48: '29.11.2019',
                  49: '30.11.2019',
                  50: '01.12.2019',
                  51: '02.12.2019',
                  52: '03.12.2019',
                  53: '04.12.2019',
                  54: '05.12.2019',
                  55: '06.12.2019',
                  56: '07.12.2019',
                  57: '08.12.2019',
                  58: '09.12.2019',
                  59: '10.12.2019',
                  60: '11.12.2019',
                  61: '12.12.2019',
                  62: '13.12.2019',
                  63: '14.12.2019',
                  64: '15.12.2019',
                  65: '16.12.2019',
                  66: '17.12.2019',
                  67: '18.12.2019',
                  68: '19.12.2019',
                  69: '20.12.2019',
                  70: '21.12.2019',
                  71: '22.12.2019',
                  72: '23.12.2019',
                  73: '24.12.2019',
                  74: '25.12.2019',
                  75: '26.12.2019',
                  76: '27.12.2019',
                  77: '28.12.2019',
                  78: '29.12.2019',
                  79: '30.12.2019',
                  80: '31.12.2019',
                  81: '01.01.2020',
                  82: '02.01.2020',
                  83: '03.01.2020',
                  84: '04.01.2020',
                  85: '05.01.2020',
                  86: '06.01.2020',
                  87: '07.01.2020',
                  88: '08.01.2020',
                  89: '09.01.2020',
                  90: '10.01.2020',
                  91: '11.01.2020',
                  92: '12.01.2020',
                  93: '13.01.2020',
                  94: '14.01.2020',
                  95: '15.01.2020',
                  96: '16.01.2020',
                  97: '17.01.2020',
                  98: '18.01.2020',
                  99: '19.01.2020',
                  100: '20.01.2020',
                  101: '21.01.2020',
                  102: '22.01.2020',
                  103: '23.01.2020',
                  104: '24.01.2020',
                  105: '25.01.2020',
                  106: '26.01.2020',
                  107: '27.01.2020',
                  108: '28.01.2020',
                  109: '29.01.2020',
                  110: '30.01.2020',
                  111: '31.01.2020'},
                 'count': {0: 19,
                  1: 12,
                  2: 13,
                  3: 18,
                  4: 13,
                  5: 19,
                  6: 15,
                  7: 14,
                  8: 12,
                  9: 6,
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                  16: 11,
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                  32: 12,
                  33: 6,
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                  50: 22,
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                  93: 69,
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                  100: 140,
                  101: 173,
                  102: 163,
                  103: 171,
                  104: 183,
                  105: 165,
                  106: 189,
                  107: 201,
                  108: 230,
                  109: 290,
                  110: 311,
                  111: 321}})
Y=np.log(df['count'])
X=df.index

# log regression

df_log=pd.DataFrame({'X':df.index,
                     'Y': np.log(df['count'])})
df_log.set_index('X', inplace = True)

reg = LinearRegression().fit(np.vstack(df_log.index), df_log['Y'])
df_log['bestfit'] = reg.predict(np.vstack(df_log.index))

df_new=pd.DataFrame({'X':df.index,
                     'Y':np.exp(df['count']),
                     'trend':np.exp(df_log['bestfit'])})

df_new.set_index('X', inplace=True)

# plotly figure setup
fig=go.Figure()
fig.add_trace(go.Bar(name='X vs Y', x=df_new.index, y=df['count']))
fig.add_trace(go.Scatter(name='line of best fit', x=df_new.index, y=df_new['trend'], mode='lines'))

# plotly figure layout
fig.update_layout(xaxis_title = 'X', yaxis_title = 'Y')

fig.show()