loc[titanic["Name"].str.len().idxmax(), "Name"] Out[12]: 'Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y ˓→Vallejo)' Based on the index name of the row (307) and the column (Name) Arbitrary functions can be applied along the axes of a DataFrame using the apply() method, which, like the de- scriptive statistics methods, takes an optional axis argument: In [150]: df.apply(np.mean) Out[150]: format compression [{'infer', 'gzip', 'bz2', 'zip', 'xz', None, dict}, default 'infer'] For on-the-fly de- compression of on-disk data. If ‘infer’, then use gzip, bz2, zip, or xz if filepath_or_buffer is path-like

loc[titanic["Name"].str.len().idxmax(), "Name"] Out[12]: 'Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y ˓→Vallejo)' Based on the index name of the row (307) and the column (Name) Arbitrary functions can be applied along the axes of a DataFrame using the apply() method, which, like the de- scriptive statistics methods, takes an optional axis argument: In [149]: df.apply(np.mean) Out[149]: format compression [{'infer', 'gzip', 'bz2', 'zip', 'xz', None, dict}, default 'infer'] For on-the-fly de- compression of on-disk data. If ‘infer’, then use gzip, bz2, zip, or xz if filepath_or_buffer is a

loc[titanic["Name"].str.len().idxmax(), "Name"] Out[12]: 'Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y ˓→Vallejo)' Based on the index name of the row (307) and the column (Name) Arbitrary functions can be applied along the axes of a DataFrame using the apply() method, which, like the de- scriptive statistics methods, takes an optional axis argument: In [149]: df.apply(np.mean) Out[149]: format compression [{'infer', 'gzip', 'bz2', 'zip', 'xz', None, dict}, default 'infer'] For on-the-fly de- compression of on-disk data. If ‘infer’, then use gzip, bz2, zip, or xz if filepath_or_buffer is a

loc[titanic["Name"].str.len().idxmax(), "Name"] Out[12]: 'Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y ˓→Vallejo)' Based on the index name of the row (307) and the column (Name) Arbitrary functions can be applied along the axes of a DataFrame using the apply() method, which, like the de- scriptive statistics methods, takes an optional axis argument: In [150]: df.apply(np.mean) Out[150]: format compression [{'infer', 'gzip', 'bz2', 'zip', 'xz', None, dict}, default 'infer'] For on-the-fly de- compression of on-disk data. If ‘infer’, then use gzip, bz2, zip, or xz if filepath_or_buffer is path-like

loc[titanic["Name"].str.len().idxmax(), "Name"] Out[12]: 'Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y ˓→Vallejo)' Based on the index name of the row (307) and the column (Name) Out[139]: In [140]: plt.legend(loc='best') Out[140]: de49ac0> 134 Chapter 2. User Guide pandas: powerful Python data analysis toolkit, Release 1.2.0 2.1 Arbitrary functions can be applied along the axes of a DataFrame using the apply() method, which, like the de- scriptive statistics methods, takes an optional axis argument: In [150]: df.apply(np.mean) Out[150]:

Arbitrary functions can be applied along the axes of a DataFrame using the apply() method, which, like the de- scriptive statistics methods, takes an optional axis argument: In [146]: df.apply(np.mean) Out[146]: sharing data across data analysis languages easy. Feather is designed to faithfully serialize and de-serialize DataFrames, supporting all of the pandas dtypes, including extension dtypes such as categorical possible while still maintaining good read performance. Parquet is designed to faithfully serialize and de-serialize DataFrame s, supporting all of the pandas dtypes, includ- ing extension dtypes such as datetime

loc[titanic["Name"].str.len().idxmax(), "Name"] Out[12]: 'Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y␣ ˓→Vallejo)' Based on the index name of the row (307) and the column (Name) precise_float : boolean, default False. Set to enable usage of higher precision (strtod) function when de- coding string to double values. Default (False) is to use fast but less precise builtin functionality : .....: .....: Giada De Laurentiis (continues on next page) 338 Chapter 2. User Guide pandas: powerful Python data

loc[titanic["Name"].str.len().idxmax(), "Name"] Out[12]: 'Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y ˓→Vallejo)' Based on the index name of the row (307) and the column (Name) Arbitrary functions can be applied along the axes of a DataFrame using the apply() method, which, like the de- scriptive statistics methods, takes an optional axis argument: In [146]: df.apply(np.mean) Out[146]: sv', sep='|', chunksize=4) In [191]: reader Out[191]: de7550> In [192]: for chunk in reader: .....: print(chunk) .....: Unnamed: 0 0 1 2 3 0 0 0.469112

plot(kind='bar', ....: rot=0, ....: ax=axs) ....: Out[15]: de98cd0> In [16]: plt.xlabel("Hour of the day"); # custom x label using matplotlib In [17]: plt.ylabel("$NO_2 loc[titanic["Name"].str.len().idxmax(), "Name"] Out[12]: 'Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y ˓→Vallejo)' Based on the index name of the row (307) and the column (Name) Arbitrary functions can be applied along the axes of a DataFrame using the apply() method, which, like the de- scriptive statistics methods, takes an optional axis argument: In [146]: df.apply(np.mean) Out[146]:

loc[titanic["Name"].str.len().idxmax(), "Name"] Out[12]: 'Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y␣ ˓→Vallejo)' Based on the index name of the row (307) and the column (Name) precise_float : boolean, default False. Set to enable usage of higher precision (strtod) function when de- coding string to double values. Default (False) is to use fast but less precise builtin functionality : .....: .....: Giada De Laurentiis .....: 2005 .....: 30.00 .....: .....: