| Name | streamlit-pandas JSON |
| Version |
0.0.8
JSON |
| download |
| home_page | |
| Summary | Create a Streamlit Pandas App |
| upload_time | 2022-12-08 14:54:59 |
| maintainer | |
| docs_url | None |
| author | WJB Mattingly |
| requires_python | |
| license | |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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# Streamlit Pandas
Streamlit Pandas is a component for the [Streamlit](https://streamlit.io/) library. It allows users to load a Pandas DataFrame and automatically generate Streamlit widgets in the sidebar. These widgets trigger filtering events within the Pandas DataFrame.
# Support
Current support only exists for DataFrame columns with strings and numbers (int64 and float64). A future update will include support for time-series data.
By default, string data generates a text_input Streamlit widget, while numerical data creates sliders with ranges preset to the minimum and maximum values for that column. Users can pass a custom dictionary for handling specific types of data, where each key is the column in the DataFrame and the value is the streamlit widget type.
Sample of a custom dictionary:
```python
create_data = {"Name": "text",
"Sex": "multiselect",
"Embarked": "multiselect",
"Ticket": "text",
"Pclass": "multiselect"}
```
The current version only supports: text, multiselect, and select.
# Installation
1. First, install Streamlit
```python
pip install streamlit
```
2. Next, install Pandas
```python
pip install pandas
```
3. Install Streamlit Pandas
```python
pip install streamlit-pandas
```
# Usage
```python
import streamlit as st
import pandas as pd
import streamlit_pandas as sp
@st.cache(allow_output_mutation=True)
def load_data():
df = pd.read_csv(file)
return df
file = "../data/titanic.csv"
df = load_data()
create_data = {"Name": "text",
"Sex": "multiselect",
"Embarked": "multiselect",
"Ticket": "text",
"Pclass": "multiselect"}
all_widgets = sp.create_widgets(df, create_data, ignore_columns=["PassengerId"])
res = sp.filter_df(df, all_widgets)
st.title("Streamlit AutoPandas")
st.header("Original DataFrame")
st.write(df)
st.header("Result DataFrame")
st.write(res)
```
This will generate the following application:
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"description": "\n\n# Streamlit Pandas\nStreamlit Pandas is a component for the [Streamlit](https://streamlit.io/) library. It allows users to load a Pandas DataFrame and automatically generate Streamlit widgets in the sidebar. These widgets trigger filtering events within the Pandas DataFrame.\n\n# Support\nCurrent support only exists for DataFrame columns with strings and numbers (int64 and float64). A future update will include support for time-series data.\n\nBy default, string data generates a text_input Streamlit widget, while numerical data creates sliders with ranges preset to the minimum and maximum values for that column. Users can pass a custom dictionary for handling specific types of data, where each key is the column in the DataFrame and the value is the streamlit widget type.\n\nSample of a custom dictionary:\n\n```python\ncreate_data = {\"Name\": \"text\",\n \"Sex\": \"multiselect\",\n \"Embarked\": \"multiselect\",\n \"Ticket\": \"text\",\n \"Pclass\": \"multiselect\"}\n```\nThe current version only supports: text, multiselect, and select.\n\n# Installation\n1. First, install Streamlit\n```python\npip install streamlit\n```\n2. Next, install Pandas\n```python\npip install pandas\n```\n3. Install Streamlit Pandas\n```python\npip install streamlit-pandas\n```\n\n# Usage\n```python\nimport streamlit as st\nimport pandas as pd\nimport streamlit_pandas as sp\n\n@st.cache(allow_output_mutation=True)\ndef load_data():\n df = pd.read_csv(file)\n return df\n\nfile = \"../data/titanic.csv\"\ndf = load_data()\ncreate_data = {\"Name\": \"text\",\n \"Sex\": \"multiselect\",\n \"Embarked\": \"multiselect\",\n \"Ticket\": \"text\",\n \"Pclass\": \"multiselect\"}\n\nall_widgets = sp.create_widgets(df, create_data, ignore_columns=[\"PassengerId\"])\nres = sp.filter_df(df, all_widgets)\nst.title(\"Streamlit AutoPandas\")\nst.header(\"Original DataFrame\")\nst.write(df)\n\nst.header(\"Result DataFrame\")\nst.write(res)\n```\nThis will generate the following application:\n\n\n\n\n",
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