babypandas 0.1.1

A restricted Pandas API

babypandas

pandas data-analysis library with a restricted API

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Simplified introductory pandas library that allows for basic tabular data-analysis with only the necessary methods and arguments. Allows for a smooth transition into pandas.

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Berkeley datascience module equivalents with babypandas:

datascience method	babypandas equivalent or close	method description
Table()	bpd.DataFrame()	empty table formation
Table().with_columns(*labels_and_values)	bpd.DataFrame().assign(**kwargs)	table from lists
table.with_columns(*labels_and_values)	df.assign(**kwargs)	adding columns
table.with_rows(rows)	df.append(other_df, ignore_index=True)
Table.read_table(filepath)	bpd.read_csv(filepath)	read in data
table.num_columns	df.shape[1]	number of columns
table.num_rows	df.shape[0]	number of rows
table.labels	df.columns	list of columns
table.relabeled(label, new_label)	df.assign(new_label=df.get(label)).drop(columns=[label])	rename columns
table.column(col)	df.get(col)	get a specific column (by name)
table.column(col).item(0)	df.get(col).iloc[0]	get a specific value in the table
table.select(col1, col2)	df.get([col1, col2])	get columns as a df
table.drop(col1, col2)	df.drop(columns=[col1, col2])	drop columns
table.sort(col)	df.sort_values(by=col)	sorts values in a dataframe by col
table.take(row_indices_or_slice)	df.take(row_indices_or_slice)	selects a single row
table.where(col, are.above(num))	df.loc[df.get(col) > num]	selects rows based on condition
table.scatter(xcol, ycol)	df.plot(kind='scatter', x=xcol, y=ycol)	plots a scatter plot
table.plot(xcol, ycol)	df.plot(x=xcol, y=ycol)	plots a line plot
table.barh(col)	df.plot(kind='barh', x=col)	plots a horizontal bar plot
table.hist(col, bins)	df.get(col).plot(kind='hist', bins=bins)	plots a histogram
table.apply(fn, col)	df.get(col).apply(fn)	apply function to a column
table.group(col)	df.groupby(col).count()	give counts of values in a col
table.group(col, agg_fn)	df.groupby(col).agg_fn.reset_index()	groups by column, aggregates with fn
table.group([col1, col2])	df.groupby([col1, col2]).count().reset_index()	groups by two cols, agg with counts
table.group([col1, col2], sum)	df.groupby[col1, col2]).sum().reset_index()	groups by two cols, agg with sum
table.join(leftcol, df2, rightcol)	df.merge(df2, left_on=leftcol, right_on=rightcol)	merges two dataframes (diff col names)
table.join(col, df2, col)	df.merge(df2, on=col)	merges two dataframes (same col names)
table.sample(n)	df.sample(n, replace=True)	sample with replacement
sample_proportions(size, distr)	np.random.multinomial(size, distr) / size	gets sample proportions of a distribution