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Pandas is built around data structures called Series and DataFrames. Data for these collections can be imported from various file formats such as comma-separated values, JSON, Parquet, SQL database tables or queries, and Microsoft Excel. [8] A Series is a 1-dimensional data structure built on top of NumPy's array.
Several 8-bit character sets (encodings) were designed for binary representation of common Western European languages (Italian, Spanish, Portuguese, French, German, Dutch, English, Danish, Swedish, Norwegian, and Icelandic), which use the Latin alphabet, a few additional letters and ones with precomposed diacritics, some punctuation, and various symbols (including some Greek letters).
A common data warehouse example involves sales as the measure, with customer and product as dimensions. In each sale a customer buys a product. The data can be sliced by removing all customers except for a group under study, and then diced by grouping by product. A dimensional data element is similar to a categorical variable in statistics.
IOB files have no place to put commonly-needed meta-data, such as the character encoding being used, the data source, internal location-markers, and so on. More powerful formats (most obviously XML , but even JSON or s-expressions ) can handle far more diverse annotations, have far less variation between implementations, and are often shorter ...
Character encoding detection, charset detection, or code page detection is the process of heuristically guessing the character encoding of a series of bytes that represent text. The technique is recognised to be unreliable [ 1 ] and is only used when specific metadata , such as a HTTP Content-Type: header is either not available, or is assumed ...
L is an optional label to be associated with the message (the label is the empty string by default and can be used to authenticate data without requiring encryption), PS is a byte string of k − m L e n − 2 ⋅ h L e n − 2 {\displaystyle k-\mathrm {mLen} -2\cdot \mathrm {hLen} -2} null-bytes.
Bold indicates a data byte which has not been altered by encoding. All non-zero data bytes remain unaltered. Green indicates a zero data byte that was altered by encoding. All zero data bytes are replaced during encoding by the offset to the following zero byte (i.e. one plus the number of non-zero bytes that follow).
An "encoding sniffing algorithm" is defined in the specification to determine the character encoding of the document based on multiple sources of input, including: Explicit user instruction; An explicit meta tag within the first 1024 bytes of the document; A byte order mark (BOM) within the first three bytes of the document