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Merged
merged 6 commits into from
Jun 21, 2019
Merged

[MRG] histogram nb #2

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584c0fd
first pass at histogram nb
emmanuelle Jun 8, 2019
8d49d2d
removed v4 flag
emmanuelle Jun 8, 2019
f9ee8b1
addressed comments
emmanuelle Jun 12, 2019
a15e562
some inlining
emmanuelle Jun 14, 2019
4d285ae
plotly.express
emmanuelle Jun 16, 2019
862c310
graph_objects, update_layout
emmanuelle Jun 20, 2019
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plotly.express
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@emmanuelle
emmanuelle committed Jun 16, 2019
commit 4d285aef58e99a4c18115062029c8ba3db7b8c6f
18 changes: 9 additions & 9 deletions notebooks/histograms.md
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Expand Up @@ -42,10 +42,10 @@ jupyter:

A [histogram](https://en.wikipedia.org/wiki/Histogram) is representation of the distribution of numerical data, where the data are binned and the count for each bin is represented.

Plotly express functions *(TODO here needs link to stable px doc entry)* take as argument a tidy [pandas DataFrame](https://pandas.pydata.org/pandas-docs/stable/getting_started/10min.html).
Plotly express functions take as argument a tidy [pandas DataFrame](https://pandas.pydata.org/pandas-docs/stable/getting_started/10min.html).

```python
import plotly_express as px
import plotly.express as px
tips = px.data.tips()
fig = px.histogram(tips, x="total_bill")
fig.show()
Expand All @@ -56,7 +56,7 @@ fig.show()
By default, the number of bins is chosen so that this number is comparable to the typical number of samples in a bin. This number can be customized, as well as the range of values.

```python
import plotly_express as px
import plotly.express as px
tips = px.data.tips()
fig = px.histogram(tips, x="total_bill", nbins=20)
fig.show()
Expand All @@ -67,7 +67,7 @@ fig.show()
The default mode is to represent the count of samples in each bin. With the `histnorm` argument, it is also possible to represent the percentage or fraction of samples in each bin (`histnorm='percent'` or `probability`), or a density histogram (the sum of bars is equal to 100, `density`), or a probability density histogram (sum equal to 1, `probability density`).

```python
import plotly_express as px
import plotly.express as px
tips = px.data.tips()
fig = px.histogram(tips, x="total_bill", histnorm='probability density')
fig.show()
Expand All @@ -76,7 +76,7 @@ fig.show()
#### Aspect of the histogram plot

```python
import plotly_express as px
import plotly.express as px
tips = px.data.tips()
fig = px.histogram(tips, x="total_bill",
title='Histogram of bills',
Expand All @@ -91,7 +91,7 @@ fig.show()
#### Several histograms for the different values of one column

```python
import plotly_express as px
import plotly.express as px
tips = px.data.tips()
fig = px.histogram(tips, x="total_bill", color="sex")
fig.show()
Expand All @@ -102,18 +102,18 @@ fig.show()
For each bin of `x`, one can compute a function of data using `histfunc`. The argument of `histfunc` is the dataframe column given as the `y` argument. Below the plot shows that the average tip increases with the total bill.

```python
import plotly_express as px
import plotly.express as px
tips = px.data.tips()
fig = px.histogram(tips, x="total_bill", y="tip", histfunc='avg')
fig.show()
```

#### Visualizing the distribution

With the `marginal` keyword, a subplot is drawn alongside the histogram, visualizing the distribution.
With the `marginal` keyword, a subplot is drawn alongside the histogram, visualizing the distribution. See [the distplot page](https://plot.ly/python/distplot/)for more examples of combined statistical representations.

```python
import plotly_express as px
import plotly.express as px
tips = px.data.tips()
fig = px.histogram(tips, x="total_bill", color="sex", marginal="rug", # can be `box`, `violin`
hover_data=tips.columns)
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