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A guide to replacing the deprecated `seaborn.distplot` function.
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{
"cells": [
{
"cell_type": "markdown",
"id": "a4a70a20-dda2-44df-9d16-159cd0d07f0f",
"metadata": {},
"source": [
"# Updating code that uses `seaborn.distplot`\n",
"\n",
"[Michael Waskom](http://mwaskom.github.io/) | May 14, 2022\n",
"\n",
"Seaborn's [`distplot`](https://seaborn.pydata.org/generated/seaborn.distplot.html) function was deprecated in v0.11.0, a release that included several new functions for plotting data distributions. Calling `distplot` on v0.11.0 or later issues a warning urging the user to update their code with one of two new functions: either [`displot`](https://seaborn.pydata.org/generated/seaborn.displot.html) (note, no `t`) or [`histplot`](https://seaborn.pydata.org/generated/seaborn.histplot.html).\n",
"\n",
"While the two new functions encompass all of the functionality present in `distplot` — and much more — they have differnet signatures and are not drop-in replacements. This notebook provides a guide to updating. But first, some questions.\n",
"\n",
"#### Why is `distplot` deprecated? I use it all the time.\n",
"\n",
"As one of the very first functions added to seaborn (well before it was developed with the expectation that otheres would use it), the `distplot` API is quite distinct from nearly all of the functions added in later releases. There is no way to reference a column of a dataframe, it does not support conditional `hue` mapping, and it does not have `x`/`y` parameters or use them to orient the distribution.\n",
"\n",
"While it is similar in spirit to [\"figure-level\"](https://seaborn.pydata.org/tutorial/function_overview.html) functions like `catplot` or `relplot`— becasue it can plot multiple kinds of distributions through one interface — `distplot` does so with a set of boolean switches rather than a `kind` parameter. And it is not integrated with a `FacetGrid`, so it cannot produce subplots.\n",
"\n",
"Therefore, when it came time to update the distributions module in v0.11.0, there was no clean way to deprecate old usage patterns and modernize the API. Deprecation / replacement was the best solution. This allowed the new functions to share a consistent API with the rest of seaborn and to be *much* more powerful.\n",
"\n",
"#### Why replace it with a nearly identically named functions?\n",
"\n",
"Yeah, this is obviously not ideal. It was totally foreseeable that people would get tripped up by `distplot`/`displot`, and I apologize for that (except to the person who sent me a long, very rude email about it). I put a lot of effort into thinking of a better name, but ultimately came up empty.\n",
"\n",
"It does fit the precedent established by `catplot` and `relplot`. And, from an [etymological perspective](https://en.wiktionary.org/wiki/distribute), it's actually the right way to split variations on \"distribute\", for whatever that's worth.\n",
"\n",
"-----\n",
"\n",
"### Guide to updating\n",
"\n",
"Here's how you can go about updating your code to use the new functions. It focuses on reproducing default plots, or plots with minimal customization. The options for customization also differ, but they are amply documented in the API examples."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "913055c8-491b-4940-82fe-92eb4f67d18b",
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns\n",
"sns.set_theme()\n",
"df = sns.load_dataset(\"penguins\")"
]
},
{
"cell_type": "markdown",
"id": "6c93aced-f285-4d8c-9eb4-962c7c837099",
"metadata": {},
"source": [
"### `distplot` vs `histplot` vs `displot`\n",
"\n",
"In its most basic invocation, `distplot` shows both a histogram (using density normalization) and a superimposed kernel density estimate:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "159a02d6-18e5-4337-a8cc-8e77c0b6b049",
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"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mwaskom/code/seaborn/seaborn/distributions.py:2532: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
" warnings.warn(msg, FutureWarning)\n"
]
},
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='flipper_length_mm', ylabel='Density'>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(df[\"flipper_length_mm\"])"
]
},
{
"cell_type": "markdown",
"id": "661c36a8-ce84-4a33-b266-69c588667848",
"metadata": {},
"source": [
"In contrast, `histplot` shows a more standard count histogram."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "47d22134-edc3-445b-a42f-3e7bb858993d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='flipper_length_mm', ylabel='Count'>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.histplot(df[\"flipper_length_mm\"])"
]
},
{
"cell_type": "markdown",
"id": "a45ca28e-9a0d-431c-8bf9-9ebc55a61054",
"metadata": {},
"source": [
"The default for `displot` is the same (as it's basically using `histplot` under the hood), although it uses and returns a `FacetGrid` rather than an `Axes` because it is a [figure-level function](https://seaborn.pydata.org/tutorial/function_overview.html#figure-level-vs-axes-level-functions):"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "248aa2c4-d3be-4e0d-96d0-5bf85e8a83dd",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x119b9d460>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.displot(df[\"flipper_length_mm\"])"
]
},
{
"cell_type": "markdown",
"id": "e549d5b4-39bb-460e-8495-d368966aabcb",
"metadata": {},
"source": [
"### Replicating basic `distplot` output\n",
"\n",
"Both new functions support the kernel density estimate line, by passing `kde=True`:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "6283c740-d442-4f3a-8d17-3ab6373e5603",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='flipper_length_mm', ylabel='Count'>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.histplot(df[\"flipper_length_mm\"], kde=True)"
]
},
{
"cell_type": "markdown",
"id": "9a27bfa4-2c06-4fd2-b645-3852ce3796d3",
"metadata": {},
"source": [
"This gets us pretty close to the `distplot` output, although the y axis is still showing counts. That was a frequently requested feature and many people may prefer it, but to more closely replicate `distplot` we can set `stat=\"density\"`:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "aa64ecb9-2bd7-45e8-b3cd-649147ecf621",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='flipper_length_mm', ylabel='Density'>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.histplot(df[\"flipper_length_mm\"], kde=True, stat=\"density\")"
]
},
{
"cell_type": "markdown",
"id": "128cbfad-cbae-4313-b5f7-8a040bf25d22",
"metadata": {},
"source": [
"Notice how the `KDE` curve spills out past the histogram in `distplot`, but only covers the actual range of the data in `histplot`. This behavior can be controlled with the `cut` parameter, which you'd need to pass through using `kde_kws`. The `histplot` default is 0, while the `distplot` default is 3 (the units are multiples of the kernel bandwidth):"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "96fc6480-5d02-499f-b1d8-3d096b8e7b97",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='flipper_length_mm', ylabel='Density'>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.histplot(\n",
" df[\"flipper_length_mm\"], kde=True,\n",
" stat=\"density\", kde_kws=dict(cut=3)\n",
")"
]
},
{
"cell_type": "markdown",
"id": "bdfc3c4c-0516-4800-985c-9449d50bcd0f",
"metadata": {},
"source": [
"The only other missing piece is that the default opacity of the `distplot` histogram is a little lower:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "75ef6c71-2f08-410b-aa11-7bafbb4a7efe",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='flipper_length_mm', ylabel='Density'>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.histplot(\n",
" df[\"flipper_length_mm\"], kde=True,\n",
" stat=\"density\", kde_kws=dict(cut=3),\n",
" alpha=.4, edgecolor=(1, 1, 1, .4),\n",
")"
]
},
{
"cell_type": "markdown",
"id": "7702373d-eb3d-4c66-95ab-c283ac9019cc",
"metadata": {},
"source": [
"One other twist to be aware of involves the default bin size. By default, `distplot` uses the [Freedman-Diaconis](https://en.wikipedia.org/wiki/Freedman%E2%80%93Diaconis_rule) rule but shows no more than 50 bars. In contrast, `histplot` entirely delegates its bin selection to numpy's `\"auto\"` default, which can use different reference rules depending on data characteristics and can choose very narrow bar widths with large datasets. You may need to specify that upper-limit (`bins=50`) to exactly reproduce previous plots."
]
},
{
"cell_type": "markdown",
"id": "37cacf8c-4e01-48c0-a7e2-40dbf1ce0d75",
"metadata": {},
"source": [
"### Showing a density curve\n",
"\n",
"While `histplot` (and `displot`) can layer a kernel density curve on top of the histogram, `distplot` can also show _only_ the density curve if the histogram is disabled:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "60223ed6-b431-457b-814c-2d099a56d82e",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mwaskom/code/seaborn/seaborn/distributions.py:2532: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
" warnings.warn(msg, FutureWarning)\n"
]
},
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='flipper_length_mm', ylabel='Density'>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(df[\"flipper_length_mm\"], hist=False)"
]
},
{
"cell_type": "markdown",
"id": "9a09d187-3539-48ca-9b56-1428d78f9c22",
"metadata": {},
"source": [
"In contrast, the modern approach would call [`kdeplot`](https://seaborn.pydata.org/generated/seaborn.kdeplot.html) directly for an axes-level plot:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "ae6d8532-7782-4982-8051-f018999cbbbd",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='flipper_length_mm', ylabel='Density'>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.kdeplot(df[\"flipper_length_mm\"])"
]
},
{
"cell_type": "markdown",
"id": "b439825e-2342-476a-9515-926e50d0a3e7",
"metadata": {},
"source": [
"Or use `kind=\"kde\"` in `displot`:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "312a4a68-3711-4e93-9369-5e5d539ce6c4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x11a0618e0>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.displot(df[\"flipper_length_mm\"], kind=\"kde\")"
]
},
{
"cell_type": "markdown",
"id": "7c942d42-b106-4a0c-90e1-db5474e807b0",
"metadata": {},
"source": [
"### Adding a rug\n",
"\n",
"One way that `distplot` and `histplot` differ is that the latter has no built-in rug plot. You would need to call `rugplot` directly:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "ed76ccea-a2d8-4b66-a978-8300e1d5e3b2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='flipper_length_mm', ylabel='Count'>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.histplot(df, x=\"flipper_length_mm\", alpha=.4)\n",
"sns.rugplot(df, x=\"flipper_length_mm\")"
]
},
{
"cell_type": "markdown",
"id": "f1cd6f02-93ee-45b8-a3de-17f15ff2c093",
"metadata": {},
"source": [
"But it's always possible to add a rug in `displot`:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "5d6968e2-de89-438f-8c66-a1e62b2555ba",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x11a061be0>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.displot(df, x=\"flipper_length_mm\", alpha=.4, rug=True)"
]
},
{
"cell_type": "markdown",
"id": "4eb2a60a-5a2e-4069-ab3c-d9cb6643a68a",
"metadata": {},
"source": [
"### Further explorations\n",
"\n",
"While it can be a pain to update code or change one's habits, I hope that the many *new* capabilities brought by `histplot` and `displot` feel worth the effort in the long run. I encourage you to read through their docs and take advantage of that power."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "seaborn-py39-latest",
"language": "python",
"name": "seaborn-py39-latest"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
@richard-hurley
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Thank you straight-forward and easy to understand. Updated my code with relative ease!

@John-Sila
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Great. Thanks for this.

@the-heclop
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the-heclop commented Jun 16, 2023

getting unexpected keyword argument 'hist' when I try to disable histogram, when I am trying to just show density curve

ax1 = sns.displot(df['price'], hist=False, color="r", label="Actual Value")
sns.displot(Yhat, hist=False, color="b", label="Fitted Values" , ax=ax1)

1195 func = getattr(self, f"set_{k}", None)
1196 if not callable(func):
-> 1197 raise AttributeError(
1198 errfmt.format(cls=type(self), prop_name=k))
1199 ret.append(func(v))
1200 if ret:

AttributeError: Rectangle.set() got an unexpected keyword argument 'hist'

@TheCuriousCurator
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TheCuriousCurator commented Jul 27, 2023

not able to do multiple plots in a 2x2 grid using ax.

hisplot works but displot doesn't.

for i, ax in enumerate(axs.reshape(-1)):
    site = sites[i]
    sns.histplot(svi_mvn_samples[site], ax=ax, label="SVI (Multivariate Normal)", kde=True)
    sns.histplot(hmc_samples[site], ax=ax, label="HMC", kde=True)
    ax.set_title(site)

image

but when I the below code it doesnt plot in grid.

for i, ax in enumerate(axs.reshape(-1)):
    site = sites[i]
    sns.displot(svi_mvn_samples[site], ax=ax, label="SVI (Multivariate Normal)", kde=True)
    sns.displot(hmc_samples[site], ax=ax, label="HMC", kde=True)
    ax.set_title(site)

image

@bahramch487
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Hello,
thanks for your guidness.
I try your help untill in[12] but i get this message name 'df ' is not defined.
please help to solve this problem.
my python version is 3.11.

@bahramch487
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sns.histplot(df, x="flipper_length_mm", alpha=.4)
Traceback (most recent call last):
File "", line 1, in
NameError: name 'df' is not defined

@bahramch487
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distplot is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either displot (a figure-level function with
similar flexibility) or kdeplot (an axes-level function for kernel density plots).

@reachbharathan
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Thanks for such a detailed briefing, I am sure there would have been lots of thoughts and efforts in the change,
greatly appreciate @mwaskom . will check the latest version and provide feedbacks if any.

@kururu-abdo
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i got this error

ValueError: Multi-dimensional indexing (e.g. obj[:, None]) is no longer supported. Convert to a numpy array before indexing instead.

ax1 = sns.distplot(df['Salary'], hist=False, color="r", label="Actual Value")
sns.distplot(yhatRedge2, hist=False, color="b", label="Fitted Values" , ax=ax1)

@osman-haider
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i am facing error
ValueError: Multi-dimensional indexing (e.g. obj[:, None]) is no longer supported. Convert to a numpy array before indexing instead.

@bahramch487
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bahramch487 commented Sep 7, 2023 via email

@Chinaskidev
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ohhh my life so great!! thanks.

@swapnak1512
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Hi All,
I am facing below errors while using seaborn displot and hisplot:
Please help me in resolving the issue. Note: Same error with sns.histplot(tips["total_bill"])

Code:
import seaborn as sns
tips = sns.load_dataset('tips')
sns.displot(tips["total_bill"])

Errors
C:\Users\xxx\PycharmProjects\HelloWorld\venv\Lib\site-packages\seaborn_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
C:\Users\xxx\PycharmProjects\HelloWorld\venv\Lib\site-packages\seaborn_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):

@zwitter689
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Really appreciate this, I imagine this takes a lot of work and the rewards are nebulous. Nonetheless, this is good stuff and your deserve kudos.

@BeUnstopable
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Hello, thanks for your guidness. I try your help untill in[12] but i get this message name 'df ' is not defined. please help to solve this problem. my python version is 3.11.

can your share your code to fix the error

@i80287
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i80287 commented Oct 19, 2023

How can I add normal distribution over histogram? Using distplot it is seaborn.distplot(series, fit=scipy.stats.norm). But none of hisplot and displot has the"fit" argument

@DavidAdonduwa
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Noted. Thanks

@JerryTsiba
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Thank you. That was useful.

@Owino-Kevin
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This is very helpful. Thank you.

@Nyantahi
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Nyantahi commented Jan 6, 2024

getting unexpected keyword argument 'hist' when I try to disable histogram, when I am trying to just show density curve

ax1 = sns.displot(df['price'], hist=False, color="r", label="Actual Value")
sns.displot(Yhat, hist=False, color="b", label="Fitted Values" , ax=ax1)

1195 func = getattr(self, f"set_{k}", None) 1196 if not callable(func): -> 1197 raise AttributeError( 1198 errfmt.format(cls=type(self), prop_name=k)) 1199 ret.append(func(v)) 1200 if ret:

AttributeError: Rectangle.set() got an unexpected keyword argument 'hist'

You have to use kind="kde" and remove hist="False" like the code below:

ax1 = sns.displot(df['price'], kind="kde", color="r", label="Actual Value")
sns.displot(Y_hat, color="b", kind="kde", label="Fitted Values", ax=ax1) 

@fanel27
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fanel27 commented Feb 18, 2024

Thank you for the explanations!

@eluseful
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eluseful commented Mar 4, 2024

Thanks, good stuff!

@Andres-Abi
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Thanks, is perfect

@Kushoza8
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Good Explaination

@vishwajeet-yaduraj
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cool, now I know what to do lol.

@abirmazumdar03
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Thank you sir...This notebook was a great help

@win7
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win7 commented Jun 12, 2024

Thanks, it was a great...

@Anwarhossen21
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Thank you sir...This notebook was a great help

@abdme777
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abdme777 commented Jul 4, 2024

Hello, thanks for your guidness. I try your help untill in[12] but i get this message name 'df ' is not defined. please help to solve this problem. my python version is 3.11.

can your share your code to fix the error

I guess you figured out that df is just the variable Mr. Waskom used for his dataset.

@Nirvana2211
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Not sure who manages this webpage , but this still refers to distplot

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