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import pandas as pd
import streamlit as st
from pandas.api.types import (
is_categorical_dtype,
is_datetime64_any_dtype,
is_numeric_dtype,
is_object_dtype,
)
def calculate_height_to_display(df):
# Calculate the height of the DataFrame display area
num_rows = df.shape[0]
row_height = 35 # Estimate of row height in pixels, adjust based on your layout/theme
header_height = 35 # Estimate of header height in pixels
calculated_height = num_rows * row_height + header_height
return calculated_height
def filter_dataframe(df: pd.DataFrame, target) -> pd.DataFrame:
"""
Adds a UI on top of a dataframe to let viewers filter columns
Args:
df (pd.DataFrame): Original dataframe
Returns:
pd.DataFrame: Filtered dataframe
"""
if(target == "datasets"):
modify = st.checkbox("Enable filters to browse ASR speech data catalog")
elif(target == "benchmarks"):
modify = st.checkbox("Enable filters to browse ASR benchmarks catalog")
else:
print("Invalid target")
if not modify:
return df
df = df.copy()
# Try to convert datetimes into a standard format (datetime, no timezone)
for col in df.columns:
if is_object_dtype(df[col]):
try:
df[col] = pd.to_datetime(df[col])
except Exception:
pass
if is_datetime64_any_dtype(df[col]):
df[col] = df[col].dt.tz_localize(None)
modification_container = st.container()
with modification_container:
to_filter_columns = st.multiselect("Filter dataframe on", df.columns)
for column in to_filter_columns:
left, right = st.columns((1, 20))
# Treat columns with < 10 unique values as categorical
if is_categorical_dtype(df[column]) or df[column].nunique() < 10:
user_cat_input = right.multiselect(
f"Values for {column}",
df[column].unique(),
default=list(df[column].unique()),
)
df = df[df[column].isin(user_cat_input)]
elif is_numeric_dtype(df[column]):
_min = float(df[column].min())
_max = float(df[column].max())
step = (_max - _min) / 100
user_num_input = right.slider(
f"Values for {column}",
min_value=_min,
max_value=_max,
value=(_min, _max),
step=step,
)
df = df[df[column].between(*user_num_input)]
elif is_datetime64_any_dtype(df[column]):
user_date_input = right.date_input(
f"Values for {column}",
value=(
df[column].min(),
df[column].max(),
),
)
if len(user_date_input) == 2:
user_date_input = tuple(map(pd.to_datetime, user_date_input))
start_date, end_date = user_date_input
df = df.loc[df[column].between(start_date, end_date)]
else:
user_text_input = right.text_input(
f"Substring or regex in {column}",
)
if user_text_input:
df = df[df[column].astype(str).str.contains(user_text_input)]
return df
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