def _nom_calcs(srs: dd.Series, head: pd.Series, cfg: Config) -> Dict[str, Any]:
"""
Computations for a nominal column in plot(df)
"""
# dictionary of data for the bar chart and related insights
data: Dict[str, Any] = {}
# value counts for barchart and uniformity insight
grps = srs.value_counts(sort=False)
if cfg.bar.enable:
# select the largest or smallest groups
data["bar"] = (
grps.nlargest(cfg.bar.bars) if cfg.bar.sort_descending else grps.nsmallest(cfg.bar.bars)
)
data["nuniq"] = grps.shape[0]
if cfg.insight.enable:
data["chisq"] = chisquare(grps.values) # chi-squared test for uniformity
data["nuniq"] = grps.shape[0] # number of unique values
data["npres"] = grps.sum() # number of present (not null) values
if not head.apply(lambda x: isinstance(x, str)).all():
srs = srs.astype(str) # srs must be a string to compute the value lengths
data["min_len"], data["max_len"] = srs.str.len().min(), srs.str.len().max()
return data
def _cont_calcs(srs: dd.Series, cfg: Config) -> Dict[str, Any]:
"""
Computations for a continuous column in plot(df)
"""
# dictionary of data for the histogram and related insights
data: Dict[str, Any] = {}
if cfg.insight.enable:
data["npres"] = srs.shape[0] # number of present (not null) values
# drop infinite values
srs = srs[~srs.isin({np.inf, -np.inf})]
# histogram
data["hist"] = da.histogram(srs, bins=cfg.hist.bins, range=(srs.min(), srs.max()))
if cfg.insight.enable:
data["chisq"] = chisquare(data["hist"][0])
data["norm"] = normaltest(data["hist"][0])
data["skew"] = skewtest(data["hist"][0])
data["nneg"] = (srs < 0).sum() # number of negative values
data["nuniq"] = srs.nunique_approx() # number of unique values
data["nzero"] = (srs == 0).sum() # number of zeros
data["nreals"] = srs.shape[0] # number of non-inf values
return data
def calc_nom_col(srs: dd.Series, first_rows: pd.Series, ngroups: int,
largest: bool) -> Dict[str, Any]:
"""
Computations for a categorical column in plot(df)
Parameters
----------
srs
srs over which to compute the barchart and insights
first_rows
first rows of the dataset read into memory
ngroups
number of groups to show in the barchart
largest
whether to show the largest or smallest groups
"""
# dictionary of data for the bar chart and related insights
data = {}
## if cfg.barchart_enable or cfg.insight.uniform_enable:
grps = srs.value_counts(sort=False)
## if cfg.barchart_enable:
## nbars = cfg.barchart_nbars
## largest = cfg.barchart_largest
# select the largest or smallest groups
data["bar"] = grps.nlargest(ngroups) if largest else grps.nsmallest(
ngroups)
## if cfg.insight.uniform_enable:
# compute a chi-squared test on the frequency distribution
data["chisq"] = chisquare(grps.values)
## if cfg.barchart_enable or cfg.insight.unique_enable:
# total number of groups
data["nuniq"] = grps.shape[0]
## if cfg.insight.missing_enable:
# number of present (not null) values
data["npres"] = grps.sum()
## if cfg.insight.unique_enable and not cfg.barchart_enable:
## data["nuniq"] = srs.nunique()
## if cfg.insight.missing_enable and not cfg.barchart_enable:
## data["npresent"] = srs.shape[0]
## if cfg.insight.constant_length_enable:
if not first_rows.apply(lambda x: isinstance(x, str)).all():
srs = srs.astype(
str) # srs must be a string to compute the value lengths
length = srs.str.len()
data["min_len"], data["max_len"] = length.min(), length.max()
return data
def calc_cont_col(srs: dd.Series, bins: int) -> Dict[str, Any]:
"""
Computations for a numerical column in plot(df)
Parameters
----------
srs
srs over which to compute the barchart and insights
bins
number of bins in the bar chart
"""
# dictionary of data for the histogram and related insights
data: Dict[str, Any] = {}
## if cfg.insight.missing_enable:
data["npres"] = srs.shape[0]
## if cfg.insight.infinity_enable:
is_inf_srs = srs.isin({np.inf, -np.inf})
data["ninf"] = is_inf_srs.sum()
# remove infinite values
srs = srs[~is_inf_srs]
## if cfg.hist_enable or config.insight.uniform_enable or cfg.insight.normal_enable:
## bins = cfg.hist_bins
data["hist"] = da.histogram(srs, bins=bins, range=[srs.min(), srs.max()])
## if cfg.insight.uniform_enable:
data["chisq"] = chisquare(data["hist"][0])
## if cfg.insight.normal_enable
data["norm"] = normaltest(data["hist"][0])
## if cfg.insight.negative_enable:
data["nneg"] = (srs < 0).sum()
## if cfg.insight.skew_enabled:
data["skew"] = skewtest(data["hist"][0])
## if cfg.insight.unique_enabled:
data["nuniq"] = srs.nunique()
## if cfg.insight.zero_enabled:
data["nzero"] = (srs == 0).sum()
return data
def cont_comps(srs: dd.Series, cfg: Config) -> Dict[str, Any]:
"""
All computations required for plot(df, Continuous)
"""
# pylint: disable=too-many-branches
data: Dict[str, Any] = {}
if cfg.stats.enable or cfg.hist.enable:
data["nrows"] = srs.shape[0] # total rows
srs = srs.dropna()
if cfg.stats.enable:
data["npres"] = srs.shape[0] # number of present (not null) values
srs = srs[~srs.isin({np.inf, -np.inf})] # remove infinite values
if cfg.hist.enable or cfg.qqnorm.enable and cfg.insight.enable:
data["hist"] = da.histogram(srs, cfg.hist.bins, (srs.min(), srs.max()))
if cfg.insight.enable:
data["norm"] = normaltest(data["hist"][0])
if cfg.hist.enable and cfg.insight.enable:
data["chisq"] = chisquare(data["hist"][0])
# compute only the required amount of quantiles
if cfg.qqnorm.enable:
data["qntls"] = srs.quantile(np.linspace(0.01, 0.99, 99))
elif cfg.stats.enable:
data["qntls"] = srs.quantile([0.05, 0.25, 0.5, 0.75, 0.95])
elif cfg.box.enable:
data["qntls"] = srs.quantile([0.25, 0.5, 0.75])
if cfg.stats.enable or cfg.hist.enable and cfg.insight.enable:
data["skew"] = skew(srs)
if cfg.stats.enable or cfg.qqnorm.enable:
data["mean"] = srs.mean()
data["std"] = srs.std()
if cfg.stats.enable:
data["min"] = srs.min()
data["max"] = srs.max()
data["nreals"] = srs.shape[0]
data["nzero"] = (srs == 0).sum()
data["nneg"] = (srs < 0).sum()
data["kurt"] = kurtosis(srs)
data["mem_use"] = srs.memory_usage(deep=True)
# compute the density histogram
if cfg.kde.enable:
# To avoid the singular matrix problem, gaussian_kde needs a non-zero std.
if not math.isclose(
dask.compute(data["min"])[0],
dask.compute(data["max"])[0]):
data["dens"] = da.histogram(srs,
cfg.kde.bins, (srs.min(), srs.max()),
density=True)
# gaussian kernel density estimate
data["kde"] = gaussian_kde(
srs.map_partitions(lambda x: x.sample(min(1000, x.shape[0])),
meta=srs))
else:
data["kde"] = None
if cfg.box.enable:
data.update(_calc_box(srs, data["qntls"], cfg))
if cfg.value_table.enable:
value_counts = srs.value_counts(sort=False)
if cfg.stats.enable:
data["nuniq"] = value_counts.shape[0]
data["value_table"] = value_counts.nlargest(cfg.value_table.ngroups)
elif cfg.stats.enable:
data["nuniq"] = srs.nunique_approx()
return data
def nom_comps(srs: dd.Series, head: pd.Series, cfg: Config) -> Dict[str, Any]:
"""
All computations required for plot(df, Nominal)
"""
# pylint: disable=too-many-branches
data: Dict[str, Any] = dict()
data["nrows"] = srs.shape[0] # total rows
srs = srs.dropna() # drop null values
grps = srs.value_counts(
sort=False) # counts of unique values in the series
data["geo"] = grps
if cfg.stats.enable or cfg.bar.enable or cfg.pie.enable:
data["nuniq"] = grps.shape[0] # total number of groups
# compute bar and pie together unless the parameters are different
if cfg.bar.enable or cfg.pie.enable:
# select the largest or smallest groups
data["bar"] = (grps.nlargest(cfg.bar.bars) if cfg.bar.sort_descending
else grps.nsmallest(cfg.bar.bars))
if cfg.bar.bars == cfg.pie.slices and cfg.bar.sort_descending == cfg.pie.sort_descending:
data["pie"] = data["bar"]
else:
data["pie"] = (grps.nlargest(cfg.pie.slices)
if cfg.pie.sort_descending else grps.nsmallest(
cfg.pie.slices))
if cfg.bar.bars == cfg.value_table.ngroups and cfg.bar.sort_descending:
data["value_table"] = data["bar"]
elif cfg.pie.slices == cfg.value_table.ngroups and cfg.pie.sort_descending:
data["value_table"] = data["pie"]
else:
data["value_table"] = grps.nlargest(cfg.value_table.ngroups)
if cfg.insight.enable:
data["chisq"] = chisquare(grps.values)
df = grps.reset_index() # dataframe with group names and counts
if cfg.stats.enable or cfg.wordlen.enable:
if not head.apply(lambda x: isinstance(x, str)).all():
srs = srs.astype(
str) # srs must be a string to compute the value lengths
if cfg.stats.enable or cfg.wordcloud.enable or cfg.wordfreq.enable:
if not head.apply(lambda x: isinstance(x, str)).all():
df[df.columns[0]] = df[df.columns[0]].astype(str)
if cfg.stats.enable:
data.update(_calc_nom_stats(srs, df, data["nrows"], data["nuniq"]))
elif cfg.wordfreq.enable and cfg.insight.enable:
data["len_stats"] = {
"Minimum": srs.str.len().min(),
"Maximum": srs.str.len().max()
}
if cfg.wordlen.enable:
lens = srs.str.len()
data["len_hist"] = da.histogram(lens, cfg.wordlen.bins,
(lens.min(), lens.max()))
if cfg.wordcloud.enable or cfg.wordfreq.enable:
if all(
getattr(cfg.wordcloud, att) == getattr(cfg.wordfreq, att)
for att in ("top_words", "stopword", "stem", "lemmatize")):
word_freqs = _calc_word_freq(
df,
cfg.wordfreq.top_words,
cfg.wordfreq.stopword,
cfg.wordfreq.lemmatize,
cfg.wordfreq.stem,
)
data["word_cnts_cloud"] = word_freqs["word_cnts"]
data["nuniq_words_cloud"] = word_freqs["nuniq_words"]
else:
word_freqs = _calc_word_freq(
df.copy(),
cfg.wordfreq.top_words,
cfg.wordfreq.stopword,
cfg.wordfreq.lemmatize,
cfg.wordfreq.stem,
)
word_freqs_cloud = _calc_word_freq(
df,
cfg.wordcloud.top_words,
cfg.wordcloud.stopword,
cfg.wordcloud.lemmatize,
cfg.wordcloud.stem,
)
data["word_cnts_cloud"] = word_freqs_cloud["word_cnts"]
data["nuniq_words_cloud"] = word_freqs["nuniq_words"]
data["word_cnts_freq"] = word_freqs["word_cnts"]
data["nwords_freq"] = word_freqs["nwords"]
return data
def cont_comps(srs: dd.Series, bins: int) -> Dict[str, Any]:
"""
This function aggregates all of the computations required for plot(df, Continuous())
Parameters
----------
srs
one numerical column
bins
the number of bins in the histogram
"""
data: Dict[str, Any] = {}
## if cfg.stats_enable or cfg.hist_enable or
# calculate the total number of rows then drop the missing values
data["nrows"] = srs.shape[0]
srs = srs.dropna()
## if cfg.stats_enable
# number of not null (present) values
data["npres"] = srs.shape[0]
# remove infinite values
srs = srs[~srs.isin({np.inf, -np.inf})]
# shared computations
## if cfg.stats_enable or cfg.hist_enable or cfg.qqplot_enable and cfg.insights_enable:
data["min"], data["max"] = srs.min(), srs.max()
## if cfg.hist_enable or cfg.qqplot_enable and cfg.ingsights_enable:
data["hist"] = da.histogram(srs,
bins=bins,
range=[data["min"], data["max"]])
## if cfg.insights_enable and (cfg.qqplot_enable or cfg.hist_enable):
data["norm"] = normaltest(data["hist"][0])
## if cfg.qqplot_enable
data["qntls"] = srs.quantile(np.linspace(0.01, 0.99, 99))
## elif cfg.stats_enable
## data["qntls"] = srs.quantile([0.05, 0.25, 0.5, 0.75, 0.95])
## elif cfg.boxplot_enable
## data["qntls"] = srs.quantile([0.25, 0.5, 0.75])
## if cfg.stats_enable or cfg.hist_enable and cfg.insights_enable:
data["skew"] = skew(srs)
# if cfg.stats_enable
data["nuniq"] = srs.nunique()
data["nreals"] = srs.shape[0]
data["nzero"] = (srs == 0).sum()
data["nneg"] = (srs < 0).sum()
data["mean"] = srs.mean()
data["std"] = srs.std()
data["kurt"] = kurtosis(srs)
data["mem_use"] = srs.memory_usage(deep=True)
## if cfg.hist_enable and cfg.insight_enable
data["chisq"] = chisquare(data["hist"][0])
# compute the density histogram
data["dens"] = da.histogram(srs,
bins=bins,
range=[data["min"], data["max"]],
density=True)
# gaussian kernel density estimate
data["kde"] = gaussian_kde(
srs.map_partitions(lambda x: x.sample(min(1000, x.shape[0])),
meta=srs))
## if cfg.box_enable
data.update(calc_box(srs, data["qntls"]))
return data
def nom_comps(
srs: dd.Series,
first_rows: pd.Series,
ngroups: int,
largest: bool,
bins: int,
top_words: int,
stopword: bool,
lemmatize: bool,
stem: bool,
) -> Dict[str, Any]:
"""
This function aggregates all of the computations required for plot(df, Nominal())
Parameters
----------
srs
one categorical column
ngroups
Number of groups to return
largest
If true, show the groups with the largest count,
else show the groups with the smallest count
bins
number of bins for the category length frequency histogram
top_words
Number of highest frequency words to show in the
wordcloud and word frequency bar chart
stopword
If True, remove stop words, else keep them
lemmatize
If True, lemmatize the words before computing
the word frequencies, else don't
stem
If True, extract the stem of the words before
computing the word frequencies, else don't
""" # pylint: disable=too-many-arguments
data: Dict[str, Any] = {}
# total rows
data["nrows"] = srs.shape[0]
# drop null values
srs = srs.dropna()
## if cfg.bar_enable or cfg.pie_enable
# counts of unique values in the series
grps = srs.value_counts(sort=False)
# total number of groups
data["nuniq"] = grps.shape[0]
# select the largest or smallest groups
data["bar"] = grps.nlargest(ngroups) if largest else grps.nsmallest(
ngroups)
## if cfg.barchart_bars == cfg.piechart_slices:
data["pie"] = data["bar"]
## else
## data["pie"] = grps.nlargest(ngroups) if largest else grps.nsmallest(ngroups)
## if cfg.insights.evenness_enable
data["chisq"] = chisquare(grps.values)
## if cfg.stats_enable
df = grps.reset_index()
## if cfg.stats_enable or cfg.word_freq_enable
if not first_rows.apply(lambda x: isinstance(x, str)).all():
srs = srs.astype(
str) # srs must be a string to compute the value lengths
df[df.columns[0]] = df[df.columns[0]].astype(str)
data.update(calc_cat_stats(srs, df, bins, data["nrows"], data["nuniq"]))
# ## if cfg.word_freq_enable
data.update(calc_word_freq(df, top_words, stopword, lemmatize, stem))
return data