sort_values和reset_index

new_titanic_survival = titanic_survival.sort_values("Age",ascending=False)
print (new_titanic_survival[0:10])
titanic_reindexed = new_titanic_survival.reset_index(drop=True)
print(titanic_reindexed.iloc[0:10])

运行结果：

自定义函数

# This function returns the hundredth item from a series
def hundredth_row(column):
    # Extract the hundredth item
    hundredth_item = column.iloc[99]
    return hundredth_item

# Return the hundredth item from each column
hundredth_row = titanic_survival.apply(hundredth_row)
print (hundredth_row)

运行结果：

非0行个数

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)

运行结果：

练习

#By passing in the axis=1 argument, we can use the DataFrame.apply() method to iterate over rows instead of columns.
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)

运行结果：

连续值离散化

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)

运行结果：

添加列

titanic_survival['age_labels'] = age_labels
age_group_survival = titanic_survival.pivot_table(index="age_labels", values="Survived")
print (age_group_survival)

运行结果：