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import altair as alt
import pandas as pd
import streamlit as st
from stqdm import stqdm
stqdm.pandas()
def plot_labels_prop(data: pd.DataFrame, label_column: str):
unique_value_limit = 100
if data[label_column].nunique() > unique_value_limit:
st.warning(
f"""
The column you selected has more than {unique_value_limit}.
Are you sure it's the right column? If it is, please note that
this will impact __Wordify__ performance.
"""
)
return
source = data[label_column].value_counts().reset_index().rename(columns={"index": "Labels", label_column: "Counts"})
source["Props"] = source["Counts"] / source["Counts"].sum()
source["Proportions"] = (source["Props"].round(3) * 100).map("{:,.2f}".format) + "%"
bars = (
alt.Chart(source)
.mark_bar()
.encode(
x=alt.X("Labels:O", sort="-y"),
y="Counts:Q",
)
)
text = bars.mark_text(align="center", baseline="middle", dy=15).encode(text="Proportions:O")
return (bars + text).properties(height=300)
def plot_nchars(data: pd.DataFrame, text_column: str):
source = data[text_column].str.len().to_frame()
plot = (
alt.Chart(source)
.mark_bar()
.encode(
alt.X(f"{text_column}:Q", bin=True, axis=alt.Axis(title="# chars per text")),
alt.Y("count()", axis=alt.Axis(title="")),
)
)
return plot.properties(height=300)
def plot_score(data: pd.DataFrame, label_col: str, label: str):
source = data.loc[data[label_col] == label].sort_values("score", ascending=False).head(100)
plot = (
alt.Chart(source)
.mark_bar()
.encode(
y=alt.Y("word:O", sort="-x"),
x="score:Q",
)
)
return plot.properties(height=max(30 * source.shape[0], 50))
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