Pandas - pandas 中的情况和默认情况

Pandas是一个强大的数据分析工具，它提供了灵活且高效的数据结构，使得数据处理和分析变得更加简单。Pandas的主要数据结构是Series和DataFrame，它们可以处理各种类型的数据，并且支持数据的索引和切片操作。

python
import pandas as pd
data = [1, 2, 3, 4, 5]
series = pd.Series(data)
print(series)

0    1
1    2
2    3
3    4
4    5
dtype: int64

python
import pandas as pd
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],
        'Age': [25, 30, 35, 40],
        'City': ['New York', 'London', 'Paris', 'Tokyo']}
df = pd.DataFrame(data)
print(df)

      Name  Age      City
0    Alice   25  New York
1      Bob   30    London
2  Charlie   35     Paris
3    David   40     Tokyo

python
import pandas as pd
data = pd.read_csv('data.csv')
print(data)

python
import pandas as pd
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],
        'Age': [25, 30, 35, 40],
        'City': ['New York', 'London', 'Paris', 'Tokyo']}
df = pd.DataFrame(data)
df.to_csv('data.csv', index=False)

python
import pandas as pd
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],
        'Age': [25, 30, 35, 40],
        'City': ['New York', 'London', 'Paris', 'Tokyo']}
df = pd.DataFrame(data)
filtered_data = df[df['Age'] > 30]
print(filtered_data)

      Name  Age   City
2  Charlie   35  Paris
3    David   40  Tokyo

python
import pandas as pd
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],
        'Age': [25, 30, 35, 40],
        'City': ['New York', 'London', 'Paris', 'Tokyo']}
df = pd.DataFrame(data)
sorted_data = df.sort_values('Age', ascending=False)
print(sorted_data)

      Name  Age      City
3    David   40     Tokyo
2  Charlie   35     Paris
1      Bob   30    London
0    Alice   25  New York

python
import pandas as pd
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],
        'Age': [25, 30, 35, 40],
        'City': ['New York', 'London', 'Paris', 'Tokyo']}
df = pd.DataFrame(data)
average_age = df['Age'].mean()
max_age = df['Age'].max()
print('Average Age:', average_age)
print('Max Age:', max_age)

Average Age: 32.5
Max Age: 40

python
import pandas as pd
import matplotlib.pyplot as plt
data = {'Year': [2010, 2011, 2012, 2013, 2014],
        'Sales': [100, 150, 200, 250, 300]}
df = pd.DataFrame(data)
df.plot(x='Year', y='Sales', kind='line')
plt.show()