[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension:v3:en:feather":3},{"resolvedFromAlias":4,"canonicalExt":5,"ext":5,"name":6,"category":7,"categoryName":8,"updatedAt":9,"popularity":10,"summary":11,"howToOs":12,"quickAnswer":18,"answerIntro":19,"whatIs":20,"description":21,"furtherReading":22,"openInstructions":41,"commonIssues":53,"securityNotes":72,"faq":76,"aliases":89,"mimeTypes":90,"relatedExtensions":91,"breadcrumbs":139,"categoryAnchor":149,"categoryFuturePath":150,"metaDescription":151,"availableHowToOs":152,"openOnDeviceLinks":153,"cannotOpenReasons":169,"cannotOpenFixes":170,"convertOptions":171,"securityAffiliateMessaging":172,"securityAffiliates":173},false,"feather","Apache Arrow Feather (Arrow IPC file) format","data","Data","2026-06-12T08:34:38.609Z",42,".feather is a binary, columnar data file used to store and exchange data frames efficiently using Apache Arrow. It is commonly opened from Python (pandas/pyarrow) or R (arrow).",[13,14,15,16,17],"windows","mac","linux","ios","android","To open .FEATHER files on Windows, use Python and run: import pandas as pd; df = pd.read_feather('path\\\\to\\\\file.feather') (this relies on an Arrow/pyarrow backend).","Open a .feather file with Python using pandas.read_feather (requires an Arrow/pyarrow backend) or with Apache Arrow in R using read/write Feather support. On most systems there is no default “double-click” app; you typically load it from code.","A .feather file stores columnar table data in the Apache Arrow IPC file format (often referred to as “Feather V2”), enabling fast reads/writes and interoperability between tools that support Arrow. In practice, it is used to exchange pandas/R data frames and other tabular datasets efficiently.","Feather is used as a lightweight, efficient interchange format for tabular data—especially data frames in analytics workflows. It is designed for fast I/O and good cross-language compatibility through Apache Arrow.\n\nIn current Arrow documentation, the “Feather V2” format corresponds to the Arrow IPC file format and uses the .feather extension. This means that many modern workflows treat .feather files as Arrow IPC files on disk.\n\nA common usage pattern is: generate or transform a table in one environment (for example, Python), write it to .feather, then load it in another environment (for example, R) with minimal overhead.",[23,26,29,32,35,38],{"title":24,"url":25},"Feather File Format — Apache Arrow (Python) v10.0.1","https://arrow.apache.org/docs/10.0/python/feather.html",{"title":27,"url":28},"Arrow Columnar Format — Apache Arrow v24.0.0 (IPC File Format / “Feather V2” mention)","https://arrow.apache.org/docs/format/Columnar.html",{"title":30,"url":31},"write_feather — Arrow R Package (Feather V2 is Arrow IPC file format)","https://arrow.apache.org/docs/9.0/r/reference/write_feather.html",{"title":33,"url":34},"pandas.read_feather — pandas documentation","https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_feather.html?highlight=read",{"title":36,"url":37},"Media Types (IANA) — official registry","https://www.iana.org/assignments/media-types/index.html",{"title":39,"url":40},"shared-mime-info-spec — freedesktop.org (how extension-to-MIME mappings work on Linux desktops)","https://www.freedesktop.org/wiki/Specifications/shared-mime-info-spec/",{"windows":42,"macos":45,"linux":48,"ios":49,"android":51},[43,44],"Use Python and run: import pandas as pd; df = pd.read_feather('path\\\\to\\\\file.feather') (this relies on an Arrow/pyarrow backend).","Alternatively, use Apache Arrow in R and read the file via Arrow’s Feather/IPC support (for example, in scripts or RStudio).",[46,47],"Use Python and run: import pandas as pd; df = pd.read_feather('path/to/file.feather') (requires Arrow/pyarrow support).","Alternatively, use Apache Arrow in R and load the file using Arrow’s Feather/IPC functionality.",[46,47],[50],"There is no common native iOS app support cited for .feather; transfer the file to a desktop environment and open it with Python (pandas/pyarrow) or R (arrow).",[52],"There is no common native Android app support cited for .feather; transfer the file to a desktop environment and open it with Python (pandas/pyarrow) or R (arrow).",[54,60,66],{"title":55,"description":56,"steps":57},"pandas cannot read the file because Arrow/pyarrow is missing","pandas.read_feather depends on an Arrow backend; if the required dependency is not available, reading will fail.",[58,59],"Install and enable an Apache Arrow/pyarrow-capable environment for your Python setup.","Re-run pandas.read_feather after confirming the Arrow dependency is available.",{"title":61,"description":62,"steps":63},"File opens but data types or columns are not as expected","Feather stores Arrow columnar data; differences in schema/type handling between writers/readers (or between older and newer toolchains) can lead to unexpected dtypes or missing metadata expectations.",[64,65],"Confirm the file was written as a Feather/Arrow IPC file (Feather V2) using the same family of Arrow-based tools.","Try reading the file using pyarrow.feather directly (Python) to inspect the table schema before converting to a pandas DataFrame.",{"title":67,"description":68,"steps":69},"The file is not actually a Feather/Arrow IPC file","Some files may be misnamed with a .feather extension; Arrow-based readers will error if the file is not in the expected IPC/Feather format.",[70,71],"Ask the data provider how the file was produced and with which library/version.","Request a re-export using an Arrow Feather/IPC writer (for example, via pyarrow.feather or the Arrow R package).",[73,74,75],".feather is a binary data format; it is not intended to contain scripts or macros like some document formats, but parsing untrusted binary files can still trigger vulnerabilities in the reader library if present—prefer up-to-date Arrow/pyarrow/arrow packages when handling untrusted files.","Treat .feather as potentially sensitive data: it commonly contains full data frame contents (including identifiers), so apply appropriate access controls and avoid sharing it publicly without review.","Because Feather is meant for efficient interchange, users often download and load it directly into analysis code; only load .feather from trusted sources and validate the dataset shape/schema before using it in automated pipelines.",[77,80,83,86],{"question":78,"answer":79},"Is .feather the same as Apache Arrow IPC?","In Arrow documentation, the IPC file format is sometimes called “Feather V2” and uses the .feather extension, so many .feather files are Arrow IPC files on disk.",{"question":81,"answer":82},"How do I read a .feather file in Python?","Use pandas.read_feather to load it into a DataFrame (with an Arrow/pyarrow backend available), or use pyarrow.feather to read it as an Arrow table.",{"question":84,"answer":85},"How do I write a .feather file from R?","Use the Apache Arrow R package’s Feather/IPC writing support (documented as writing a Feather file that is an Arrow IPC file).",{"question":87,"answer":88},"Is there an official MIME type for .feather?","The IANA media type registry is the authoritative source for registered MIME types; the provided sources do not indicate a specific registered IANA media type for Feather.",[],[],[92,98,104,109,115,121,127,133],{"ext":93,"name":94,"category":7,"categoryName":8,"popularity":95,"summary":96,"howToOs":97},"json","JSON data",88,".json files store structured data as plain text using the JSON (JavaScript Object Notation) format. They’re commonly used for web APIs, app configuration, and data exchange, and can be opened with any text editor or JSON-aware tools.",[13,14,15,16,17],{"ext":99,"name":100,"category":7,"categoryName":8,"popularity":101,"summary":102,"howToOs":103},"db","SQLite 3 Database",85,".db is commonly used for SQLite 3 database files—single-file relational databases used by many apps. You can open them with SQLite tools and many database browsers.",[13,14,15,16,17],{"ext":105,"name":106,"category":7,"categoryName":8,"popularity":101,"summary":107,"howToOs":108},"yaml","YAML File",".yaml is a YAML document: a human-readable data serialization format commonly used for configuration files and data exchange. You can open it with a text editor, or with editor support such as the YAML extension for Visual Studio Code.",[13,14,15,16,17],{"ext":110,"name":111,"category":7,"categoryName":8,"popularity":112,"summary":113,"howToOs":114},"sqlite3","SQLite 3 database",78,".sqlite3 is a SQLite version 3 database stored as a single cross-platform file. You can open it with SQLite’s sqlite3 command-line shell or any SQLite-capable database browser/library.",[13,14,15,16,17],{"ext":116,"name":117,"category":7,"categoryName":8,"popularity":118,"summary":119,"howToOs":120},"sqlite","SQLite 3 Database File",75,".sqlite is a SQLite 3 database stored as a single file. You open it with SQLite tools (for example, the SQLite command-line shell) or any application that supports SQLite.",[13,14,15,16,17],{"ext":122,"name":123,"category":7,"categoryName":8,"popularity":124,"summary":125,"howToOs":126},"jsonl","JSON Lines (newline-delimited JSON)",72,".jsonl is a JSON Lines file: plain text where each line is a complete JSON value, commonly used for logs and large datasets that are processed record-by-record.",[13,14,15,16,17],{"ext":128,"name":129,"category":7,"categoryName":8,"popularity":130,"summary":131,"howToOs":132},"parquet","Apache Parquet columnar data file",70,".parquet is an open, column-oriented file format for efficient storage and retrieval of tabular data. It’s commonly opened with data tools such as pandas (Python) and other analytics/ETL software that supports Apache Parquet.",[13,14,15,16,17],{"ext":134,"name":135,"category":7,"categoryName":8,"popularity":136,"summary":137,"howToOs":138},"ndjson","Newline-Delimited JSON (NDJSON)",55,"NDJSON is a text format where each line is a complete JSON object, making it convenient for streaming and log-like datasets. You can open it with any text editor, and many data tools can process it line-by-line.",[13,14,15,16,17],[140,143,146],{"label":141,"to":142},"Home","/",{"label":144,"to":145},"File Extension Index","/file-extension",{"label":147,"to":148},".FEATHER","/file-extension/feather","category-data","/category/data","Learn what .FEATHER files are, how to open them on every platform, common fixes, and security best practices.",[13,14,15,16,17],[154,157,160,163,166],{"os":13,"label":155,"to":156},"Open .FEATHER on Windows","/how-to/open-feather-on-windows",{"os":14,"label":158,"to":159},"Open .FEATHER on Mac","/how-to/open-feather-on-mac",{"os":15,"label":161,"to":162},"Open .FEATHER on Linux","/how-to/open-feather-on-linux",{"os":16,"label":164,"to":165},"Open .FEATHER on iOS","/how-to/open-feather-on-ios",{"os":17,"label":167,"to":168},"Open .FEATHER on Android","/how-to/open-feather-on-android",[55,61,67],[58,59],[],"untrusted_source",[174,178],{"name":175,"description":176,"affiliateUrl":177},"Avast","Avast offers free and premium antivirus software that protects against viruses, malware, ransomware, and phishing. Scan files before opening them to ensure safety.","https://www.avast.com/lp-aff-consumer-store?expid=inf601",{"name":179,"description":180,"affiliateUrl":181},"Norton","Norton 360 delivers comprehensive antivirus protection, VPN, and identity theft monitoring. Scan files for threats before opening to keep your device secure.","http://buy.norton.com/aff_home?utm_campaign=en-ww_nor_n36_aff_nas_nau_nah_cj_nad_low:_sec_nat_mktc_norton_360"]