(~series) operators (GH2686) • DataFrame.plot now includes a logx parameter to change the x-axis to log scale (GH2327) • Series arithmetic operators can now handle constant and ndarray input (GH2574) • ExcelFile matplotlib pandas: powerful Python data analysis toolkit, Release 0.12.0 You may pass logy to get a log-scale Y axis. In [11]: plt.figure(); In [11]: ts = Series(randn(1000), index=date_range(’1/1/2000’, periods=1000)) plot(secondary_y=[’A’, ’B’]) In [23]: ax.set_ylabel(’CD scale’) In [24]: ax.right_ax.set_ylabel(’AB scale’) 322 Chapter 16. Plotting

categories in one step (which has some speed advantage), or simply set the categories to a predefined scale, use set_categories(). In [85]: s = pd.Series(["one", "two", "four", "-"], dtype="category") In pandas: powerful Python data analysis toolkit, Release 0.25.3 Scales You may pass logy to get a log-scale Y axis. In [118]: ts = pd.Series(np.random.randn(1000), .....: index=pd.date_range('1/1/2000', periods=1000)) plot(secondary_y=['A', 'B']) In [125]: ax.set_ylabel('CD scale') Out[125]: Text(0, 0.5, 'CD scale') In [126]: ax.right_ax.set_ylabel('AB scale') Out[126]: Text(0, 0.5, 'AB scale') 4.10. Visualization 587 pandas: powerful

(~series) operators (GH2686) • DataFrame.plot now includes a logx parameter to change the x-axis to log scale (GH2327) • Series arithmetic operators can now handle constant and ndarray input (GH2574) • ExcelFile #include #include typedef struct _Data { int32_t count; double avg; float scale; } Data; int main(int argc, const char *argv[]) { size_t n = 10; 172 Chapter 7. Cookbook pandas: Release 0.14.0 Data d[n]; for (int i = 0; i < n; ++i) { d[i].count = i; d[i].avg = i + 1.0; d[i].scale = (float) i + 2.0f; } FILE *file = fopen("binary.dat", "wb"); fwrite(&d, sizeof(Data), n, file);

(~series) operators (GH2686) • DataFrame.plot now includes a logx parameter to change the x-axis to log scale (GH2327) • Series arithmetic operators can now handle constant and ndarray input (GH2574) • ExcelFile count; double avg; float scale; } Data; int main(int argc, const char *argv[]) { size_t n = 10; Data d[n]; for (int i = 0; i < n; ++i) { d[i].count = i; d[i].avg = i + 1.0; d[i].scale = (float) i + 2.0f; where each element of the struct corresponds to a column in the frame: names = ’count’, ’avg’, ’scale’ # note that the offsets are larger than the size of the type because of # struct padding offsets

(~series) operators (GH2686) • DataFrame.plot now includes a logx parameter to change the x-axis to log scale (GH2327) • Series arithmetic operators can now handle constant and ndarray input (GH2574) • ExcelFile count; double avg; float scale; } Data; int main(int argc, const char *argv[]) { size_t n = 10; Data d[n]; for (int i = 0; i < n; ++i) { d[i].count = i; d[i].avg = i + 1.0; d[i].scale = (float) i + 2.0f; where each element of the struct corresponds to a column in the frame: names = ’count’, ’avg’, ’scale’ # note that the offsets are larger than the size of the type because of # struct padding offsets

(~series) operators (GH2686) • DataFrame.plot now includes a logx parameter to change the x-axis to log scale (GH2327) • Series arithmetic operators can now handle constant and ndarray input (GH2574) • ExcelFile count; double avg; float scale; } Data; int main(int argc, const char *argv[]) { size_t n = 10; Data d[n]; for (int i = 0; i < n; ++i) { d[i].count = i; d[i].avg = i + 1.0; d[i].scale = (float) i + 2.0f; where each element of the struct corresponds to a column in the frame: names = 'count', 'avg', 'scale' # note that the offsets are larger than the size of the type because of # struct padding offsets

(~series) operators (GH2686) • DataFrame.plot now includes a logx parameter to change the x-axis to log scale (GH2327) • Series arithmetic operators can now handle constant and ndarray input (GH2574) • ExcelFile matplotlib pandas: powerful Python data analysis toolkit, Release 0.13.1 You may pass logy to get a log-scale Y axis. In [11]: plt.figure(); In [12]: ts = Series(randn(1000), index=date_range(’1/1/2000’, periods=1000)) plot(secondary_y=[’A’, ’B’]) In [24]: ax.set_ylabel(’CD scale’) Out[24]: In [25]: ax.right_ax.set_ylabel(’AB scale’) Out[25]: 418

length=1033)) Some other advantages are the convenient subsetting of time period or the adapted time scale on plots. Let’s apply this on our data. Create a plot of the ??2 values in the different stations categories in one step (which has some speed advantage), or simply set the categories to a predefined scale, use set_categories(). In [85]: s = pd.Series(["one", "two", "four", "-"], dtype="category") In pandas: powerful Python data analysis toolkit, Release 1.1.1 Scales You may pass logy to get a log-scale Y axis. In [120]: ts = pd.Series(np.random.randn(1000), .....: index=pd.date_range('1/1/2000', periods=1000))

length=1033)) Some other advantages are the convenient subsetting of time period or the adapted time scale on plots. Let’s apply this on our data. Create a plot of the ??2 values in the different stations categories in one step (which has some speed advantage), or simply set the categories to a predefined scale, use set_categories(). In [85]: s = pd.Series(["one", "two", "four", "-"], dtype="category") In pandas: powerful Python data analysis toolkit, Release 1.1.0 Scales You may pass logy to get a log-scale Y axis. In [120]: ts = pd.Series(np.random.randn(1000), .....: index=pd.date_range('1/1/2000', periods=1000))

categories in one step (which has some speed advantage), or simply set the categories to a predefined scale, use set_categories(). In [85]: s = pd.Series(["one", "two", "four", "-"], dtype="category") In pandas: powerful Python data analysis toolkit, Release 1.0.0 Scales You may pass logy to get a log-scale Y axis. In [118]: ts = pd.Series(np.random.randn(1000), .....: index=pd.date_range('1/1/2000', periods=1000)) plot(secondary_y=['A', 'B']) In [125]: ax.set_ylabel('CD scale') Out[125]: Text(0, 0.5, 'CD scale') In [126]: ax.right_ax.set_ylabel('AB scale') Out[126]: Text(0, 0.5, 'AB scale') 622 Chapter 3. User Guide pandas: powerful