size=50)}) .....: In [125]: df5.mode() Out[125]: A B 0 4 8 Discretization and quantiling Continuous values can be discretized using the cut() (bins based on values) and qcut() (bins based on sample cut() function computes groupings for the values of the input array and is often used to transform continuous variables to discrete or categorical variables: In [80]: ages = np.array([10, 15, 13, 12, 23, Aggregation and plotting time series Turn a matrix with hours in columns and days in rows into a continuous row sequence in the form of a time series. How to rearrange a Python pandas DataFrame? Dealing

.: In [125]: df5.mode() Out[125]: A B 0 0 -1 1 2 7 2 5 12 Discretization and quantiling Continuous values can be discretized using the cut() (bins based on values) and qcut() (bins based on sample cut() function computes groupings for the values of the input array and is often used to transform continuous variables to discrete or categorical variables: In [80]: ages = np.array([10, 15, 13, 12, 23, Aggregation and plotting time series Turn a matrix with hours in columns and days in rows into a continuous row sequence in the form of a time series. How to rearrange a Python pandas DataFrame? Dealing

df5.mode() Out[125]: A B 0 4.0 -9 1 NaN -8 2 NaN -6 3 NaN 11 Discretization and quantiling Continuous values can be discretized using the cut() (bins based on values) and qcut() (bins based on sample cut() function computes groupings for the values of the input array and is often used to transform continuous variables to discrete or categorical variables: In [80]: ages = np.array([10, 15, 13, 12, 23, Aggregation and plotting time series Turn a matrix with hours in columns and days in rows into a continuous row sequence in the form of a time series. How to rearrange a Python pandas DataFrame? 4.20. Cookbook

[125]: df5.mode() Out[125]: A B 0 1.0 -9 1 NaN 10 2 NaN 13 Discretization and quantiling Continuous values can be discretized using the cut() (bins based on values) and qcut() (bins based on sample cut() function computes groupings for the values of the input array and is often used to transform continuous variables to discrete or categorical variables: In [80]: ages = np.array([10, 15, 13, 12, 23, Aggregation and plotting time series Turn a matrix with hours in columns and days in rows into a continuous row sequence in the form of a time series. How to rearrange a Python pandas DataFrame? Dealing

[125]: df5.mode() Out[125]: A B 0 1.0 -9 1 NaN 10 2 NaN 13 Discretization and quantiling Continuous values can be discretized using the cut() (bins based on values) and qcut() (bins based on sample cut() function computes groupings for the values of the input array and is often used to transform continuous variables to discrete or categorical variables: In [80]: ages = np.array([10, 15, 13, 12, 23, Aggregation and plotting time series Turn a matrix with hours in columns and days in rows into a continuous row sequence in the form of a time series. How to rearrange a Python pandas DataFrame? Dealing

[128]: df5.mode() Out[128]: A B 0 1.0 -9 1 NaN 10 2 NaN 13 Discretization and quantiling Continuous values can be discretized using the cut() (bins based on values) and qcut() (bins based on sample cut() function computes groupings for the values of the input array and is often used to transform continuous variables to discrete or categorical variables: In [86]: ages = np.array([10, 15, 13, 12, 23, Aggregation and plotting time series Turn a matrix with hours in columns and days in rows into a continuous row sequence in the form of a time series. How to rearrange a Python pandas DataFrame? Dealing

[128]: df5.mode() Out[128]: A B 0 1.0 -9 1 NaN 10 2 NaN 13 Discretization and quantiling Continuous values can be discretized using the cut() (bins based on values) and qcut() (bins based on sample cut() function computes groupings for the values of the input array and is often used to transform continuous variables to discrete or categorical variables: In [86]: ages = np.array([10, 15, 13, 12, 23, Aggregation and plotting time series Turn a matrix with hours in columns and days in rows into a continuous row sequence in the form of a time series. How to rearrange a Python pandas DataFrame? Dealing

size=50)}) .....: In [129]: df5.mode() Out[129]: A B 0 0 -9 Discretization and quantiling Continuous values can be discretized using the cut() (bins based on values) and qcut() (bins based on sample cut() function computes groupings for the values of the input array and is often used to transform continuous variables to discrete or categorical variables: 4.5. Reshaping and Pivot Tables 509 pandas: powerful Aggregation and plotting time series Turn a matrix with hours in columns and days in rows into a continuous row sequence in the form of a time series. How to rearrange a Python pandas DataFrame? Dealing

[125]: df5.mode() Out[125]: A B 0 1.0 -9 1 NaN 10 2 NaN 13 Discretization and quantiling Continuous values can be discretized using the cut() (bins based on values) and qcut() (bins based on sample cut() function computes groupings for the values of the input array and is often used to transform continuous variables to discrete or categorical variables: In [80]: ages = np.array([10, 15, 13, 12, 23, Aggregation and plotting time series Turn a matrix with hours in columns and days in rows into a continuous row sequence in the form of a time series. How to rearrange a Python pandas DataFrame? Dealing

Compatibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386 3.5.2 Testing With Continuous Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386 3.5.3 Test-driven New cut and qcut functions (like R’s cut function) for computing a categorical variable from a continuous variable by binning values either into value-based (cut) or quantile-based (qcut) bins • Rename base – Code standards * C (cpplint) * Python (PEP8) * Backwards Compatibility – Testing With Continuous Integration – Test-driven development/code writing * Writing tests * Transitioning to pytest