Python-Pandas(4)自定义函数方法

最新推荐文章于 2025-07-07 21:56:17 发布

原创最新推荐文章于 2025-07-07 21:56:17 发布 · 2.4k 阅读

3 ·

CC 4.0 BY-SA版权

文章标签：

#pandas

python 专栏收录该内容

32 篇文章

订阅专栏

本文通过Python Pandas库对泰坦尼克号乘客数据进行处理，包括删除含有缺失值的行或列、筛选特定条件下的数据、按年龄排序并重置索引等操作，并定义了多个函数用于分析数据，例如统计每列中缺失值的数量、判断乘客等级和年龄状态等。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

#specifying axis=1 or axis='columns' will drop any columns that have null values
drop_na_columns = titanic_survival.dropna(axis=1)
new_titanic_survival = titanic_survival.dropna(axis=0,subset=["Age", "Sex"])
#print new_titanic_survival

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

这里写图片描述

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

这里写图片描述

# 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

这里写图片描述

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

这里写图片描述

#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

这里写图片描述

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

这里写图片描述

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

这里写图片描述