Pandas

最新推荐文章于 2025-05-26 22:43:36 发布

班达learning

最新推荐文章于 2025-05-26 22:43:36 发布

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分类专栏： # numpy

本文链接：https://blog.youkuaiyun.com/Iam_Human/article/details/102545831

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本文详细介绍了使用Python的Pandas库进行数据操作的基础知识。包括Series和DataFrame的创建、索引、选择数据的方法，以及如何通过标签或整数位置访问单个值或一组行和列。适合Pandas初学者快速上手。

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pandas.Series

一维数组/向量

定义

a = pd.Series([1, 2, 3, 'a']) # 不指定索引
>> 0    1
	1    2
	2    3
	3    a
	dtype: object
a = pd.Series([1, 2, 3, 'a'], index=['a', 'b', 'c','d']) # 指定索引
>> a    1
b    2
c    3
d    a
dtype: object
print(a.index)
>> Index(['a', 'b', 'c', 'd'], dtype='object')

a['a'] >> 1

a[['a']]  #注意与上面的区别
>>
a    1
dtype: object

a[['a','d']]  
>> 
a    1
d    a
dtype: object

3.1 pandas.DataFrame

3.1.1 Constructor

# Constructor
d = {'uage':['35-40', '25-30'], 'ugender':['male', 'female'], 'ulevel':[3, 4]}
df = pd.DataFrame(data = d)
"""
    uage ugender  ulevel
0  35-40    male       3
1  25-30  female       4
"""

# Constructor_from_numpy
n = np.array([['35-40', 'male', 3], ['25-30', 'female', 4]])
df2 = pd.DataFrame(n, columns=['uage', 'ugender', 'ulevel'])

# DataFrame.Attribute
print("index:",df.index)     #index: RangeIndex(start=0, stop=2, step=1)
print("columns:",df.columns)  # columns: Index(['uage', 'ugender', 'ulevel'], dtype='object')
print("dtype:\n",df.dtypes)

3.1.2 index

# .at :  access a single value
# .iat:  access a single value by integer position
print(df.at[1, 'ulevel']) # 4
df.at[1, 'ulevel']=5
print(df.at[1, 'ulevel']) # 5
print(df.iat[1, 2])     # 5

# .loc: access a group of rows and columns by labels or a boolean array
df3 = pd.DataFrame(n, index=['0105', '0106'], columns=['uage', 'ugender', 'ulevel'])
print("only one row:\n",df3.loc['0105'])
print("\nmultiple rows:\n", df3.loc[['0105', '0106']])  #注意与下面的差别
print("\nrow and column:\n", df3.loc['0105', 'ugender'])
print("\nmultiple columns:\n", df3.loc[:, ['ugender', 'uage']])

print("\n其他索引方式\n")

print("Boolean:\n",df.loc[[False, True]]) # same length as the row axis