pandas 自定义函数方法

# --------------------dropna(axis=1)去掉有缺失值的行------------------------------------------ drop_na_columns = titanic_survival.dropna(axis=1) new_titanic_survival = titanic_survival.dropna(axis=0,subset=["Age", "Sex"]) #在age和sex列进行dropna操作 print(new_titanic_survival) # ---------------------------打印83行age列的值和766行pclass列的值------------------------------- row_index_83_age = titanic_survival.loc[83,"Age"] row_index_1000_pclass = titanic_survival.loc[766,"Pclass"] print(row_index_83_age) print(row_index_1000_pclass) #---------------------sort_values函数是排序，表示已age列为准进行降序排列--------------------------- new_titanic_survival = titanic_survival.sort_values("Age",ascending=False) print(new_titanic_survival[0:10]) # -------------------------------重新定义序号------------------------------------------ itanic_reindexed = new_titanic_survival.reset_index(drop=True) print(titanic_reindexed.iloc[0:10]) # --------------------------------------找出第100行------------------------------------------------ def hundredth_row(column): hundredth_item = column.loc[99] return hundredth_item hundredth_row = titanic_survival.apply(hundredth_row) print(hundredth_row) # ---------------------------------打印出每行空值的个数---------------------------------------------- def not_null_count(column): column_null = pd.isnull(column) null = column[column_null] return len(null) column_null_count = titanic_survival.apply(not_null_count) print(column_null_count) def which_class(row): pclass = row['Pclass'] if pd.isnull(pclass): return "Unknown" elif pclass == 1: return "First Class" elif pclass == 2: return "Second Class" elif pclass == 3: return "Third Class" classes = titanic_survival.apply(which_class, axis=1) print(classes) def is_minor(row): if row["Age"] < 18: return True else: return False minors = titanic_survival.apply(is_minor, axis=1) #print minors def generate_age_label(row): age = row["Age"] if pd.isnull(age): return "unknown" elif age < 18: return "minor" else: return "adult" age_labels = titanic_survival.apply(generate_age_label, axis=1) print(age_labels) titanic_survival['age_labels'] = age_labels age_group_survival = titanic_survival.pivot_table(index="age_labels", values="Survived") print(age_group_survival)