Historical and sociological research on naming practices- Fischer, Sue and Telles ,Īnd Lieberson are three among countless examples-and because they create a useful context for practicing routines such as column selection or drawing time series. These data are chosen for their connection to existing We focus on a historical dataset consisting of records in the naming of children from the (MetadataĪccompanying text documents is often stored in a tabular format.) As example data here, Often complement text datasets like those analyzed in previous chapters. This chapter provides aĭetailed account of how scholars can use the library to load, manipulate, and analyze tabular data. In this chapter, we review an external library, “ Pandas”, which wasīriefly touched upon in chapter Introduction. This chapter demonstrates the standard methods for analyzing tabular data in Python in the context of a case study in onomastics, a field devoted to the study of naming practices. ![]() Tabular datasets are often viewed in a spreadsheet program such as LibreOffice Calc or Microsoft Excel. Each record is associated with a fixed number of fields. Tabular datasets organize machine-readable data (numbers and strings) into a sequence of records. Data-intensive research in the humanities and allied social sciences in general is far more likely to feature the analysis of tabular data than text documents. Data analysis in literary studies tends to involve the analysis of text documents (see chapters chp-vector-space-model and chp-getting-data).
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