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Build an interactive Netflix-title browser in Python with Streamlit filters, a searchable results table, and Plotly charts—all grounded in one dated CSV snapshot. The example below checks the file’s columns before enabling filters, handles missing values explicitly, and keeps chart counts and table rows tied to the same filtered data.
Choose and identify one Netflix titles snapshot
Common “Netflix Titles” CSV files are third-party historical snapshots, not official, live Netflix catalogs. Pick one exact file, verify its publisher’s reuse terms, and identify its source and snapshot date in the app. The source descriptions available for two versions are not interchangeable:
| Version described | Snapshot and size | Fields and caveats |
|---|---|---|
| James Oruhu’s Kaggle writeup (2026) | Describes a late-2021 snapshot with 8,807 records. | Lists title, type, director, cast, country, release year, rating, duration, genres, and description; reports more than 4,300 missing entries. These figures describe that particular file, not Netflix’s current catalog. Source |
| Onyx Data DataDNA challenge description (2021) | Describes an April 2021 dataset with 7,787 rows and 12 columns. | Lists show_id, type, title, director, cast, country, date_added, release_year, rating, duration, listed_in, and description. Source |
The source descriptions do not establish reuse rights for the specific CSV you may download. Check the dataset publisher’s terms before using or redistributing it; do not assume the file can be bundled with an app. In the example, place a CSV you are permitted to use at data/netflix_titles.csv, and replace the source label and snapshot date with those for that file.
Set up the Streamlit app
Install Streamlit, pandas, and Plotly in your Python environment:
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python -m pip install streamlit pandas plotlySave the following as app.py. It normalizes column names, parses numeric years and dates conservatively, and creates filters only when the corresponding fields exist.
from pathlib import Path
import pandas as pd
import plotly.express as px
import streamlit as st
CSV_PATH = Path("data/netflix_titles.csv")
SOURCE_LABEL = "Replace with the CSV publisher and exact dataset version"
SNAPSHOT_LABEL = "Replace with the snapshot date stated by that publisher"
st.set_page_config(page_title="Netflix Catalog Explorer", layout="wide")
st.title("Netflix Catalog Explorer")
st.caption(f"Source: {SOURCE_LABEL} · Snapshot: {SNAPSHOT_LABEL}. This is a historical dataset, not a live Netflix catalog.")
if not CSV_PATH.exists():
st.error(f"CSV not found: {CSV_PATH}. Place your permitted dataset file there, or update CSV_PATH.")
st.stop()
try:
df = pd.read_csv(CSV_PATH)
except Exception as exc:
st.error(f"Could not read the CSV: {exc}")
st.stop()
# Normalize headers for reliable field lookup while retaining the original data.
df.columns = [str(c).strip().lower().replace(" ", "_") for c in df.columns]
if df.columns.duplicated().any():
st.error("The CSV has duplicate column names after normalization. Rename the conflicting headers and reload it.")
st.stop()
for col in ("release_year",):
if col in df.columns:
df[col] = pd.to_numeric(df[col], errors="coerce")
if "date_added" in df.columns:
df["date_added"] = pd.to_datetime(df["date_added"], errors="coerce")
st.sidebar.header("Filters")
filtered = df.copy()
def choose_values(label, column):
global filtered
if column not in filtered.columns:
return
values = sorted(filtered[column].dropna().astype(str).unique().tolist())
if not values:
st.sidebar.caption(f"No non-missing values available for {label.lower()}.")
return
selected = st.sidebar.multiselect(label, values)
if selected:
filtered = filtered[filtered[column].astype(str).isin(selected)]
choose_values("Content type", "type")
if "release_year" in filtered.columns:
years = filtered["release_year"].dropna()
if not years.empty:
low, high = int(years.min()), int(years.max())
year_range = st.sidebar.slider("Release-year range", low, high, (low, high))
filtered = filtered[filtered["release_year"].between(*year_range) | filtered["release_year"].isna()]
choose_values("Country", "country")
choose_values("Rating", "rating")
choose_values("Category / genre", "listed_in")
search = st.sidebar.text_input("Search title or description")
if search.strip():
mask = pd.Series(False, index=filtered.index)
for col in ("title", "description"):
if col in filtered.columns:
mask |= filtered[col].fillna("").astype(str).str.contains(search.strip(), case=False, regex=False)
filtered = filtered[mask]
st.subheader("Filtered catalog")
st.write(f"{len(filtered):,} rows match the current filters.")
# Charts and the table all use the same filtered dataframe.
if "type" in filtered.columns and not filtered.empty:
counts = filtered["type"].fillna("Missing").value_counts().rename_axis("type").reset_index(name="titles")
st.plotly_chart(px.bar(counts, x="type", y="titles", title="Titles by content type"), use_container_width=True)
if "release_year" in filtered.columns:
years = filtered["release_year"].dropna()
if not years.empty:
st.plotly_chart(px.histogram(filtered.dropna(subset=["release_year"]), x="release_year", nbins=30,
title="Titles by release year"), use_container_width=True)
if "date_added" in filtered.columns:
additions = filtered.dropna(subset=["date_added"]).assign(added_year=lambda x: x["date_added"].dt.year)
if not additions.empty:
counts = additions.groupby("added_year").size().rename("titles").reset_index()
st.plotly_chart(px.bar(counts, x="added_year", y="titles", title="Dataset entries by date-added year"), use_container_width=True)
if "country" in filtered.columns:
countries = (filtered.dropna(subset=["country"])["country"].astype(str)
.str.split(",").explode().str.strip())
countries = countries[countries.ne("")].value_counts().head(15).rename_axis("country").reset_index(name="titles")
if not countries.empty:
st.plotly_chart(px.bar(countries.sort_values("titles"), x="titles", y="country", orientation="h",
title="Most-listed countries (top 15)"), use_container_width=True)
if "listed_in" in filtered.columns:
categories = (filtered.dropna(subset=["listed_in"])["listed_in"].astype(str)
.str.split(",").explode().str.strip())
categories = categories[categories.ne("")].value_counts().head(15).rename_axis("category").reset_index(name="titles")
if not categories.empty:
st.plotly_chart(px.bar(categories.sort_values("titles"), x="titles", y="category", orientation="h",
title="Most-listed categories (top 15)"), use_container_width=True)
st.dataframe(filtered, use_container_width=True, hide_index=True)Run it and understand what the filters do
- Put the permitted CSV at
data/netflix_titles.csv, or changeCSV_PATHto its location. - Set
SOURCE_LABELandSNAPSHOT_LABELto match the exact file you selected; do not use one version’s row count or date for another. - Start the app with
streamlit run app.py. Streamlit opens the app in a browser; the sidebar controls narrow the displayed catalog. - Use the search box to match title or description text without regard to letter case. Missing descriptions are treated as empty text for searching.
The release-year slider uses the observed non-missing minimum and maximum in the currently filtered data. Rows with missing release years remain included, since a year range cannot classify them. Missing values are not offered as country, rating, type, or category filter choices. The results count and table reflect the final filtered dataframe; charts are calculated from that same dataframe.
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Choose charts that answer real catalog questions
How many movies and TV shows are in the filtered file?
The content-type bar chart groups by the type field. Missing types are labeled “Missing” in the chart rather than silently discarded. It describes rows in the selected CSV, not the current Netflix offering.
When were the titles released?
The histogram uses release_year, coerced to numeric values; unparseable values become missing and are left out of this chart. The release year is not the date Netflix added a title to the catalog.
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When does the file say titles were added?
If present, date_added is parsed as a date and grouped by calendar year for the addition chart. It is a different field from release_year. Invalid or missing dates do not contribute to the chart.
Which countries and categories appear most often?
Country and listed_in values in common files can contain comma-separated entries. The example splits those fields and counts a row once for each listed country or category. Therefore, totals across those bars can exceed the number of filtered rows; they are counts of associations, not mutually exclusive title counts. The charts show only the 15 most frequent values to keep labels readable.
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Use chart selection only when another view needs it
Streamlit’s st.plotly_chart reference accepts a Plotly Figure or Data object. By default, chart selection events are ignored. Set on_select="rerun" or provide a callback when a selection should affect another part of the app; selection modes include points, box, and lasso. For example:
event = st.plotly_chart(
figure,
key="catalog_chart",
on_select="rerun",
selection_mode=("points", "box", "lasso"),
)
selected_points = event.selection
Selection state is read-only: use it to respond in other views, not as a value to edit directly. Leave selection disabled when charts are purely summaries. The same reference notes that charts with more than 1,000 points may use WebGL rendering, so avoid oversized point-level charts when a grouped bar chart or histogram answers the question more clearly. Plotly.py is an interactive, open-source Python graphing library; its Python documentation covers chart families including bars, histograms, lines, scatter plots, and heatmaps.
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What this explorer can and cannot tell you
This app is an exploratory browser for the selected CSV. It is not a recommendation engine, and its contents do not establish what Netflix currently offers in a particular country. Dataset schemas, collection dates, row counts, and missingness vary between files, so keep the source and snapshot visible and build filters only for columns that actually exist.
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