pandas.cut¶ pandas.cut (x, bins, right=True, labels=None, retbins=False, precision=3, include_lowest=False, duplicates='raise') [source] ¶ Bin values into discrete intervals. Use cut when you need to segment and sort data values into bins. This function is also useful for going from a continuous variable to a categorical variable.

So qcut ensures a more even distribution of the values in each bin even if they cluster in the sample space. This means you are less likely to have a bin full of data with very close values and another bin with 0 values. pandas.qcut¶ pandas.qcut (x, q, labels=None, retbins=False, precision=3, duplicates='raise') [source] ¶ Quantile-based discretization function. Discretize variable into equal-sized buckets based on rank or based on sample quantiles.

qcut. The pandas documentation describes qcut as a “Quantile-based discretization function.” This basically means that qcut tries to divide up the underlying data into equal sized bins. The function defines the bins using percentiles based on the distribution of the data, not the actual numeric edges of the bins.

qcut. The pandas documentation describes qcut as a “Quantile-based discretization function.” This basically means that qcut tries to divide up the underlying data into equal sized bins. The function defines the bins using percentiles based on the distribution of the data, not the actual numeric edges of the bins. qcut. The pandas documentation describes qcut as a “Quantile-based discretization function.” This basically means that qcut tries to divide up the underlying data into equal sized bins. The function defines the bins using percentiles based on the distribution of the data, not the actual numeric edges of the bins.

The following are code examples for showing how to use pandas.qcut().They are from open source Python projects. You can vote up the examples you like or vote down the ones you don't like. pandas.qcut¶ pandas.qcut (x, q, labels=None, retbins=False, precision=3, duplicates='raise') [source] ¶ Quantile-based discretization function. Discretize variable into equal-sized buckets based on rank or based on sample quantiles.

So qcut ensures a more even distribution of the values in each bin even if they cluster in the sample space. This means you are less likely to have a bin full of data with very close values and another bin with 0 values.

qcut. The pandas documentation describes qcut as a “Quantile-based discretization function.” This basically means that qcut tries to divide up the underlying data into equal sized bins. The function defines the bins using percentiles based on the distribution of the data, not the actual numeric edges of the bins.

pandas.cut¶ pandas.cut (x, bins, right=True, labels=None, retbins=False, precision=3, include_lowest=False, duplicates='raise') [source] ¶ Bin values into discrete intervals. Use cut when you need to segment and sort data values into bins. This function is also useful for going from a continuous variable to a categorical variable. pandas.cut¶ pandas.cut (x, bins, right=True, labels=None, retbins=False, precision=3, include_lowest=False, duplicates='raise') [source] ¶ Bin values into discrete intervals. Use cut when you need to segment and sort data values into bins. This function is also useful for going from a continuous variable to a categorical variable. So qcut ensures a more even distribution of the values in each bin even if they cluster in the sample space. This means you are less likely to have a bin full of data with very close values and another bin with 0 values.

So qcut ensures a more even distribution of the values in each bin even if they cluster in the sample space. This means you are less likely to have a bin full of data with very close values and another bin with 0 values.

qcut. The pandas documentation describes qcut as a “Quantile-based discretization function.” This basically means that qcut tries to divide up the underlying data into equal sized bins. The function defines the bins using percentiles based on the distribution of the data, not the actual numeric edges of the bins.

pandas.qcut¶ pandas.qcut (x, q, labels=None, retbins=False, precision=3, duplicates='raise') [source] ¶ Quantile-based discretization function. Discretize variable into equal-sized buckets based on rank or based on sample quantiles. pandas.qcut¶ pandas.qcut (x, q, labels=None, retbins=False, precision=3, duplicates='raise') [source] ¶ Quantile-based discretization function. Discretize variable into equal-sized buckets based on rank or based on sample quantiles.

qcut. The pandas documentation describes qcut as a “Quantile-based discretization function.” This basically means that qcut tries to divide up the underlying data into equal sized bins. The function defines the bins using percentiles based on the distribution of the data, not the actual numeric edges of the bins. qcut. The pandas documentation describes qcut as a “Quantile-based discretization function.” This basically means that qcut tries to divide up the underlying data into equal sized bins. The function defines the bins using percentiles based on the distribution of the data, not the actual numeric edges of the bins.