Pandas-DataFrame

一、Pandas-DataFrame特点

二、特征练习

# 从列表嵌套字典创建DataFrame
data = [{'a': 1, 'b': 2},{'a': 5, 'b': 10, 'c': 20}]
df = pd.DataFrame(data)
print(df)
print('-----------')

# 从字典创建DataFrame
data = {'Name':['Tom', 'Jack', 'Steve', 'Ricky'],'Age':[28,34,29,42]}
df = pd.DataFrame(data, index=['s1','s2','s3','s4'])
print(df)
print('-----------')

# 从字典创建DataFrame
data = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']),
        'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])}
df = pd.DataFrame(data)
print(df)
print('-----------')

#从数组中读取数据
data=[['zhaosan',12],['lisi',15],['wangwu',19]]
df=pd.DataFrame(data,columns=['name','age'])
print(df)
print('-----------')

   a   b     c
0  1   2   NaN
1  5  10  20.0
-----------
     Name  Age
s1    Tom   28
s2   Jack   34
s3  Steve   29
s4  Ricky   42
-----------
   one  two
a  1.0    1
b  2.0    2
c  3.0    3
d  NaN    4
-----------
      name  age
0  zhaosan   12
1     lisi   15
2   wangwu   19
-----------

执行结果

data={
    'Name': ['Alice', 'Bob', 'Charlie', 'David'],
    'Age': [25, 30, 35, 40],
    'City': ['New York', 'Los Angeles', 'Chicago', 'Houston']
}

df=pd.DataFrame(data)

# 查看前两行数据
print(df.head(2))
print("-----------------")

# 查看 DataFrame 的基本信息
print(df.info())
print("-----------------")

# 获取描述统计信息
print(df.describe())
print("-----------------")

# 按年龄排序
df_sorted = df.sort_values(by='Age', ascending=False)
print(df_sorted)
print("-----------------")

# 选择指定列
print(df[['Name', 'Age']])
print("-----------------")

# 按索引选择行
print(df.iloc[1:3])  # 选择第二到第三行（按位置）
print("-----------------")

# 按标签选择行
print(df.loc[:2])  # 选择第二到第三行（按标签）
print("-----------------")

# 计算分组统计（按城市分组，计算平均年龄）
print(df.groupby('City')['Age'].mean())
print("-----------------")

# 处理缺失值（填充缺失值）
df.loc[2,'Age']=None
print(df)
print("-----------------")
df['Age'] = df['Age'].fillna(30)
print(df)
print("-----------------")

# 导出为 CSV 文件
df.to_csv('output.csv', index=False)

  执行结果：

    Name  Age         City
0  Alice   25     New York
1    Bob   30  Los Angeles
-----------------
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4 entries, 0 to 3
Data columns (total 3 columns):
 #   Column  Non-Null Count  Dtype 
---  ------  --------------  ----- 
 0   Name    4 non-null      object
 1   Age     4 non-null      int64 
 2   City    4 non-null      object
dtypes: int64(1), object(2)
memory usage: 224.0+ bytes
None
-----------------
             Age
count   4.000000
mean   32.500000
std     6.454972
min    25.000000
25%    28.750000
50%    32.500000
75%    36.250000
max    40.000000
-----------------
      Name  Age         City
3    David   40      Houston
2  Charlie   35      Chicago
1      Bob   30  Los Angeles
0    Alice   25     New York
-----------------
      Name  Age
0    Alice   25
1      Bob   30
2  Charlie   35
3    David   40
-----------------
      Name  Age         City
1      Bob   30  Los Angeles
2  Charlie   35      Chicago
-----------------
      Name  Age         City
0    Alice   25     New York
1      Bob   30  Los Angeles
2  Charlie   35      Chicago
-----------------
City
Chicago        35.0
Houston        40.0
Los Angeles    30.0
New York       25.0
Name: Age, dtype: float64
-----------------
      Name   Age         City
0    Alice  25.0     New York
1      Bob  30.0  Los Angeles
2  Charlie   NaN      Chicago
3    David  40.0      Houston
-----------------
      Name   Age         City
0    Alice  25.0     New York
1      Bob  30.0  Los Angeles
2  Charlie  30.0      Chicago
3    David  40.0      Houston
-----------------