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  2. pandas (software) - Wikipedia

    en.wikipedia.org/wiki/Pandas_(software)

    However, if data is a DataFrame, then data['a'] returns all values in the column(s) named a. To avoid this ambiguity, Pandas supports the syntax data.loc['a'] as an alternative way to filter using the index. Pandas also supports the syntax data.iloc[n], which always takes an integer n and returns the nth value, counting from 0. This allows a ...

  3. Wes McKinney - Wikipedia

    en.wikipedia.org/wiki/Wes_McKinney

    Wes McKinney is an American software developer and businessman. He is the creator and "Benevolent Dictator for Life" (BDFL) of the open-source pandas package for data analysis in the Python programming language, and has also authored three versions of the reference book Python for Data Analysis.

  4. Dataframe - Wikipedia

    en.wikipedia.org/wiki/Dataframe

    Dataframe may refer to: A tabular data structure common to many data processing libraries: pandas (software) § DataFrames; The Dataframe API in Apache Spark; Data frames in the R programming language; Frame (networking)

  5. Data analysis - Wikipedia

    en.wikipedia.org/wiki/Data_analysis

    By splitting the data into multiple parts, we can check if an analysis (like a fitted model) based on one part of the data generalizes to another part of the data as well. [144] Cross-validation is generally inappropriate, though, if there are correlations within the data, e.g. with panel data . [ 145 ]

  6. Pearson correlation coefficient - Wikipedia

    en.wikipedia.org/wiki/Pearson_correlation...

    The Pandas and Polars Python libraries implement the Pearson correlation coefficient calculation as the default option for the methods pandas.DataFrame.corr and polars.corr, respectively. Wolfram Mathematica via the Correlation function, or (with the P value) with CorrelationTest. The Boost C++ library via the correlation_coefficient function.

  7. Data modeling - Wikipedia

    en.wikipedia.org/wiki/Data_modeling

    The ANSI/SPARC three level architecture. This shows that a data model can be an external model (or view), a conceptual model, or a physical model. This is not the only way to look at data models, but it is a useful way, particularly when comparing models. [1] In 1975 ANSI described three kinds of data-model instance: [5]

  8. Hydrophobic collapse - Wikipedia

    en.wikipedia.org/wiki/Hydrophobic_collapse

    Hydrophobic collapse is a proposed process for the production of the 3-D conformation adopted by polypeptides and other molecules in polar solvents. The theory states that the nascent polypeptide forms initial secondary structure (ɑ-helices and β-strands) creating localized regions of predominantly hydrophobic residues.

  9. Exploratory data analysis - Wikipedia

    en.wikipedia.org/wiki/Exploratory_data_analysis

    Tukey defined data analysis in 1961 as: "Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data."