--- title: "How to read a JSON file in Python (faster than pandas)" date: "2026-06-08" author: "Al Brown" category: "Engineering" excerpt: "Read an NDJSON or JSON file into a DataFrame with chDB, a drop-in replacement for pandas. Change one import line and your existing pandas code runs on ClickHouse's engine, so it stays fast as files grow." --- # How to read a JSON file in Python (faster than pandas) To read a JSON file in Python, use [chDB](/resources/engineering/what-is-chdb) as a drop-in replacement for pandas. Change one import line and the code you already write keeps working: ```python import chdb.datastore as pd df = pd.read_json("data/events.ndjson", lines=True) # NDJSON: one object per line ``` `df` supports the pandas API you already use (indexing, filtering, `groupby`, `merge`), but the work runs on ClickHouse's engine instead of pandas, so it stays fast as files grow. There is no server to start and no separate load step. ```bash pip install chdb ``` Prefer the command line? See [how to run SQL on a JSON file](/resources/engineering/run-sql-on-json-file) to do this with clickhouse local. ## Read an NDJSON file into a DataFrame Most "JSON" data you query in bulk is NDJSON (newline-delimited JSON, also called json-lines): one JSON object per line. Pass `lines=True` and chDB reads it directly, inferring column types from the data: ```python import chdb.datastore as pd df = pd.read_json("data/events.ndjson", lines=True) print(df) print(df.dtypes) ``` ```text event_id country event_type revenue user items 0 1 GB purchase 49.99 [101, gold] [sku-a, sku-b] 1 2 AU view 0.00 [102, silver] [sku-c] 2 3 GB purchase 19.50 [103, gold] [sku-a] 3 4 IN purchase 5.00 [104, bronze] [sku-d, sku-e, sku-f] 4 5 AU purchase 99.00 [105, gold] [] event_id int64 country str event_type str revenue float64 user object items object dtype: object ``` chDB inferred the types automatically: `event_id` is an integer, `revenue` is a float. Nested objects (`user`) and arrays (`items`) survive as `object` columns. You did not declare a single dtype. One thing is different from pandas under the hood: `df` is not the whole file loaded into memory. It is a lazy, ClickHouse-backed object that records what you asked for and runs it on ClickHouse's engine when you materialize a result: print it, take `len()`, or call `.to_pandas()`. That is why it stays fast on large files: it only computes the rows and columns you actually use. A top-level JSON array (a `.json` file whose outer structure is `[{...}, {...}]`) reads without `lines=True`: ```python arr_df = pd.read_json("data/events_array.json") ``` ## Filter and aggregate the way you already do The pandas you write does not change. Use a boolean mask to filter, then `groupby` and aggregate: ```python import chdb.datastore as pd df = pd.read_json("data/events.ndjson", lines=True) purchases = (df[df["event_type"] == "purchase"] .groupby("country")["revenue"].sum() .sort_values(ascending=False)) print(purchases) ``` ```text country AU 99.00 GB 69.49 IN 5.00 Name: revenue, dtype: float64 ``` Same syntax as pandas, same result. chDB compiles the whole chain into one optimized query and only scans the columns the aggregation touches, rather than materializing the full file first. ## Nested objects: extract fields with .apply When chDB reads an NDJSON file with nested objects, those objects come back as struct columns (arrays of values in field order). After calling `.to_pandas()`, use `.apply` to pull out the fields you need: ```python import pandas as real_pd pdf = df.to_pandas() pdf["user_id"] = pdf["user"].apply(lambda x: x[0]) pdf["user_tier"] = pdf["user"].apply(lambda x: x[1]) print(pdf[["event_id", "user_id", "user_tier", "revenue"]]) ``` ```text event_id user_id user_tier revenue 0 1 101 gold 49.99 1 2 102 silver 0.00 2 3 103 gold 19.50 3 4 104 bronze 5.00 4 5 105 gold 99.00 ``` ## Nested arrays: flatten with .explode Arrays come back as list columns. Call `.explode()` on a real pandas DataFrame to turn each element into its own row: ```python pdf = df.to_pandas() exploded = (pdf[["event_id", "items"]] .explode("items") .dropna(subset=["items"]) .reset_index(drop=True)) print(exploded) ``` ```text event_id items 0 1 sku-a 1 1 sku-b 2 2 sku-c 3 3 sku-a 4 4 sku-d 5 4 sku-e 6 4 sku-f ``` ## Hand off to real pandas when you need it When a library downstream needs an actual pandas object (scikit-learn, a plotting call, anything that mutates in place), call `.to_pandas()` to materialize one: ```python result = df[df["event_type"] == "purchase"].groupby("country")["revenue"].sum() pdf_result = result.to_pandas() # a real pandas.Series, in memory ``` The usual pattern is to do the heavy filtering and aggregation on the chDB object, where it is fast, then `.to_pandas()` the small result and continue in ordinary pandas. ## Is it faster than `pandas.read_json`? On a file big enough that scanning is the actual cost, yes. JSON is a text format, so most of the work is parsing bytes into typed values, and chDB does that in compiled, multi-threaded ClickHouse code rather than the Python layer. The same filter-and-aggregate above on a 2M-row (~146 MB) NDJSON file, best-of-3 with a warm page cache on an Apple M4 Pro (14 cores, 24 GB RAM, macOS); chDB 4.1.8, Python 3.14; best-of-3, warm: ```text import pandas as pd: 1.714s import chdb.datastore as pd: 0.134s speedup: 12.8x ``` About 13x here, from the same code with one import changed. The back-to-back ratio is the robust claim; absolute times shift with cache state and concurrent load. The gap widens as files grow, because plain pandas parses and holds the whole file in memory before it can group anything, while chDB streams and aggregates in one pass. The small case can cut the other way: on a tiny JSON file (a few thousand rows) plain pandas is as fast or faster, because chDB pays a fixed per-query startup cost that dominates when there is barely any data. Reach for chDB when the file is large, or when you want one import to cover both. ## Works in Jupyter Printing a chDB object renders it as a table in a notebook, and `.to_pandas()` feeds straight into `.plot()`, joins, or any pandas code. The companion folder ships a `run.ipynb` you can open and run cell by cell. ## Run it yourself The complete, runnable example is here, with `generate.sh` to create the sample JSON files plus `run.ipynb` and a `run.py` mirror containing the exact code above: [github.com/ClickHouse/examples/tree/main/local-analytics/chdb-json](https://github.com/ClickHouse/examples/tree/main/local-analytics/chdb-json) Working with other formats? [Read a CSV file in Python](/resources/engineering/read-csv-file-python) and [read a Parquet file in Python](/resources/engineering/read-parquet-file-python) use the same drop-in `import chdb.datastore as pd` pattern; only the reader changes. If you keep re-reading the same JSON, [convert it to Parquet](/resources/engineering/convert-json-to-parquet) once and every later read gets faster. The same code scales from a JSON file on your laptop to a server to [ClickHouse Cloud](https://clickhouse.com/cloud) with no rewrite, so there is nothing to re-learn when the data outgrows one machine.