#TABULAR DATA DEFINITION HOW TO#

The structure of the chapter is as follows. Literature that aim to provide explanations for the shift in naming practices. Using Python and Pandas, and, on the other hand, to replicate some of the analyses in the

#TABULAR DATA DEFINITION HOW TO#

The chapter is, one the one hand, to show how to map out such examples of cultural change We do not seek to find a conclusiveĮxplanation for the observed shifts in the current chapter. Literacy, and decline of the nuclear family).

Sociological, demographic, and cultural changes (e.g., industrialization, spread of Underlying this shift in naming practices, because of its co-occurrence with numerous Lieberson, Lieberson and Lynn, it is extremely difficult to pinpoint the factors Of change in leading names has significantly increased. Over long periods of time, more recent data exhibit similarities with fashion trends (see, e.g., Acerbi et al. Rather conservative naming practices, with barely any changes in the most popular names Given to children over the past two centuries. One of the key observations he makes concerns an accelerating rate of change in the names Shifts in child naming practices from a cross-cultural and socio-historical perspective. In his book A Matter of Taste, Lieberson examines long-term The reader will, for instance, learn to make histograms and line plots using the Pandas DataFrame methods plot() or hist().Īs this chapter’s case study, we will examine diachronic developments in child naming The essentials of data visualization are also introduced, on which subsequent chapters in the book will draw. We will cover in detail a number of high-level functions from the Pandas package, such as the convenient groupby() method and methods for splitting and merging datasets.

The material presented here should be especially useful for scholars coming from a different scripting language background (e.g., R or Matlab), where similar manipulation routines exist. 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 Exploring Texts using the Vector Space Model and Parsing and Manipulating Structured Data).

Tabular data definition