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Update histograms.md #2242

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Mar 6, 2020
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Update histograms.md #2242

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Update histograms.md
SylwiaOliwia2 Mar 3, 2020
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Update histograms.md
SylwiaOliwia2 Mar 6, 2020
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26 changes: 26 additions & 0 deletions doc/python/histograms.md
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Expand Up @@ -69,6 +69,32 @@ fig = px.histogram(df, x="total_bill", nbins=20)
fig.show()
```

#### Accessing the y-axis values
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Maybe Accessing the counts (y-axis) values?


JavaScript calculates the y-axis (count) values on the fly in the browser, so it's not accessible in the `fig`. You can manually calculate it using `pandas.cut` or `np.digitize`.

```python
import plotly.express as px
import pandas as pd

df = px.data.tips()

# create the bins; use the `range` to get the same bin size as in the histogram in the previous section.
df["total_bill_bins"] = pd.cut(df.total_bill, bins=range(0, 60, 5), right=False)

# calculate counts
df_counts = df.pivot_table(index="total_bill_bins", values="size", aggfunc='count').reset_index()
df_counts.rename(columns={"size": "count"}, inplace=True)

# sort, then convert to string
df_counts = df_counts.sort_values(by="total_bill_bins")
df_counts["total_bill_bins"] = df_counts["total_bill_bins"].astype(str)

# display calculated counts on the bar chart
fig = px.bar(df_counts, x="total_bill_bins", y="count")
fig.show()
```

#### Type of normalization

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`).
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