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You can build an interactive Netflix title catalog browser with Streamlit filters, a Plotly chart, and a results table—all driven by the same filtered DataFrame. The example below uses a CSV you supply; its snapshot date, publisher, and reuse terms must be stated in the app because popular Netflix-title files are historical third-party snapshots, not a live or official Netflix catalog.
Choose and identify the CSV before analyzing it
Netflix-title CSVs with similar names can describe different snapshots. For example, James Oruhu’s 2026 writeup describes an 8,807-record file as a late-2021 snapshot, while Onyx Data’s archived April 2021 challenge dataset reports 7,787 rows and 12 columns. These are descriptions of distinct third-party files, not counts of Netflix’s current catalog; they should not be combined or treated as a measured change in Netflix’s inventory. See Oruhu’s dataset writeup and Onyx Data’s April 2021 dataset description.
For this tutorial, use the April 2021 Onyx Data version, whose documented columns include title, type, country, date_added, release_year, rating, duration, listed_in, and description. Obtain the CSV from its publisher and check the terms attached to that exact download before using or redistributing it. The available dataset description does not establish a current reuse license, so do not assume the file can be republished or bundled with your app. In the code, set the snapshot label to match the actual file you obtained; do not use this example’s date if you selected a different version.
Set up a small Streamlit app
Install the Python packages in your environment, place the CSV at netflix_titles.csv beside the app, and save the following as app.py:
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import pandas as pd
import plotly.express as px
import streamlit as st
CSV_PATH = "netflix_titles.csv"
SOURCE_LABEL = "Onyx Data: Netflix Movies and TV Shows challenge dataset"
SNAPSHOT_LABEL = "April 2021"
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 third-party dataset, not a live Netflix catalog."
)
@st.cache_data
def load_data(path):
df = pd.read_csv(path)
# Normalize headings to simplify checks and references below.
df.columns = [str(c).strip().lower() for c in df.columns]
if "date_added" in df.columns:
df["date_added"] = pd.to_datetime(df["date_added"], errors="coerce")
if "release_year" in df.columns:
df["release_year"] = pd.to_numeric(df["release_year"], errors="coerce")
return df
df = load_data(CSV_PATH)
# Only offer filters for fields present in this particular CSV.
filtered = df.copy()
with st.sidebar:
st.header("Filters")
if "type" in filtered.columns:
values = sorted(filtered["type"].dropna().astype(str).unique())
chosen = st.multiselect("Content type", values, default=values)
filtered = filtered[filtered["type"].astype(str).isin(chosen)]
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.slider("Release year", low, high, (low, high))
filtered = filtered[
filtered["release_year"].between(year_range[0], year_range[1])
]
if "country" in filtered.columns:
countries = sorted(
{part.strip() for value in filtered["country"].dropna().astype(str)
for part in value.split(",") if part.strip()}
)
chosen_countries = st.multiselect("Country (matches any listed value)", countries)
if chosen_countries:
pattern = "|".join(
__import__("re").escape(value) for value in chosen_countries
)
filtered = filtered[
filtered["country"].fillna("").str.contains(pattern, case=False, regex=True)
]
if "rating" in filtered.columns:
ratings = sorted(filtered["rating"].dropna().astype(str).unique())
chosen_ratings = st.multiselect("Rating", ratings)
if chosen_ratings:
filtered = filtered[filtered["rating"].astype(str).isin(chosen_ratings)]
if "listed_in" in filtered.columns:
categories = sorted(
{part.strip() for value in filtered["listed_in"].dropna().astype(str)
for part in value.split(",") if part.strip()}
)
chosen_categories = st.multiselect("Category (matches any listed value)", categories)
if chosen_categories:
pattern = "|".join(
__import__("re").escape(value) for value in chosen_categories
)
filtered = filtered[
filtered["listed_in"].fillna("").str.contains(pattern, case=False, regex=True)
]
query = st.text_input("Search title or description")
if query:
searchable = pd.Series(False, index=filtered.index)
for column in ("title", "description"):
if column in filtered.columns:
searchable |= filtered[column].fillna("").astype(str).str.contains(
query, case=False, regex=False
)
filtered = filtered[searchable]
st.subheader(f"{len(filtered):,} matching rows")
if "type" in filtered.columns and not filtered.empty:
mix = filtered["type"].fillna("Missing").value_counts().rename_axis("Type").reset_index(name="Titles")
st.plotly_chart(
px.bar(mix, x="Type", y="Titles", title="Movies and TV shows in the filtered results"),
use_container_width=True,
)
if "release_year" in filtered.columns and not filtered.empty:
yearly = (
filtered.dropna(subset=["release_year"])
.groupby("release_year").size().rename("Titles").reset_index()
)
st.plotly_chart(
px.histogram(
yearly, x="release_year", y="Titles", histfunc="sum", nbins=30,
title="Titles by release year in the filtered results",
labels={"release_year": "Release year"},
),
use_container_width=True,
)
if "date_added" in filtered.columns and filtered["date_added"].notna().any():
additions = (
filtered.dropna(subset=["date_added"])
.assign(added_year=lambda x: x["date_added"].dt.year)
.groupby("added_year").size().rename("Titles").reset_index()
)
st.plotly_chart(
px.bar(additions, x="added_year", y="Titles", title="Recorded additions by date_added year"),
use_container_width=True,
)
# Keep the table aligned with the filtered rows and show only available columns.
display_columns = [c for c in ["title", "type", "release_year", "country", "rating", "duration", "listed_in", "date_added"] if c in filtered.columns]
st.dataframe(filtered[display_columns] if display_columns else filtered, use_container_width=True, hide_index=True)
Run it from the directory containing both files with streamlit run app.py. The app caption names the chosen source and snapshot so that a reader does not mistake its results for a current availability listing. The April 2021 schema distinguishes date_added from release_year: the former records an addition date, when present, while the latter is the title’s release year. Parsing with errors set to coerce leaves unparseable or missing values as missing rather than inventing dates or years.
How do the filters and missing values work?
The sidebar filters are conditional: a control appears only when its column exists. Country and listed_in values are split on commas to populate options, and choosing one matches a row if that value occurs anywhere in its list. The chart and table count rows, not individual country or category memberships; a multi-country title therefore contributes one row to the overall count even though it can match more than one country selection.
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Missing values are not offered as country, rating, or category options. The type chart labels a missing type as “Missing,” while release-year and addition-year charts omit rows without usable dates or years. This is important because a writeup of the separate late-2021 8,807-record file reports more than 4,300 missing entries. That figure applies to that file and writeup, not automatically to the April 2021 CSV or another download. Inspect missingness in the exact file you load before interpreting a chart.
Text search checks title and description independently when those columns exist. It is case-insensitive and treats the entered text literally rather than as a regular expression. All filters are applied to one DataFrame, so the displayed matching-row count, charts, and table are based on the same subset.
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- Shows on the go: Take your TV to-go when traveling—without needing to log into someone else’s device.
Choose Plotly charts for the questions in the data
The example uses bars for content-type mix and recorded additions, and a binned distribution for release years. Plotly.py supports interactive charts across forms including bars, histograms, lines, scatter plots, and heatmaps; the useful chart is the one suited to the question and the available columns, not the most elaborate one. See Plotly’s Python documentation.
- Type mix: compares the number of filtered rows per content type.
- Release-year distribution: shows the spread of release years in the filtered rows; it does not say when Netflix added a title.
- Recorded additions: groups valid
date_addedvalues by year. It describes dates recorded in the snapshot, not a complete history of Netflix releases. - Country or category comparison: can be added from
countryorlisted_in. Decide whether rows count once per title or once for every listed value; if memberships are expanded, label counts as memberships rather than unique titles.
Streamlit renders a Plotly figure through st.plotly_chart. Its documentation accepts a Plotly Figure or Data object and describes the on_select options "ignore", "rerun", or a callback. Selection is ignored by default. To make selecting chart marks update another view, enable selection handling and use the returned selection state; documented selection modes include points, box, and lasso. The selection state is read-only, so it can drive downstream filtering but is not a writable control. More than 1,000 points may use WebGL rendering. Check the current Streamlit chart reference against the version installed for the app.
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What this explorer can—and cannot—tell you
This app is a way to explore the fields and records in one identified CSV. It is not a recommendation engine, and it cannot establish whether a title is currently offered in a particular country or account. The dataset descriptions establish different historical snapshots, not a consistent method for comparing inventory over time. Present any counts or patterns as findings about the chosen file and its snapshot date.
Quick Recap
Best Value
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- Seamless streaming for any room: With Roku Streaming Stick 4K, watch your favorite entertainment on any TV in the house, even in rooms farther from your router thanks to the long-range Wi-Fi receiver.
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- Compact without compromises: Our sleek design won’t block neighboring HDMI ports, so you can switch from streaming to gaming with ease. Plus, it’s designed to stay hidden behind your TV, keeping wires neatly out of sight
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