df = df.set_index(keys=["year", "month"])
时间: 2023-07-06 10:29:07 浏览: 42
这段代码是将 DataFrame 的两列数据作为行索引,其中 `keys=["year", "month"]` 表示将 DataFrame 中的 `"year"` 和 `"month"` 两列作为行索引的两个层级,从而形成一个多层次索引。
这样做的好处是可以更方便地对数据进行分组、筛选和聚合操作。例如,如果想要按照年份进行分组,可以使用 `df.groupby("year")`;如果想要选择某个特定的月份的数据,可以使用 `df.loc[(year, month)]`,其中 `year` 和 `month` 分别是想要选择的年份和月份的值。
需要注意的是,如果原来的 DataFrame 中已经有了行索引,那么这个操作会替换掉原来的行索引,如果想要保留原来的行索引,可以先使用 `reset_index()` 将行索引转成列,然后再使用 `set_index()` 将新的索引设置为行索引。
相关问题
mport pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns df = pd.read_csv('data(北深).csv') df['date'] = pd.to_datetime(df['date']) # 将日期字符串转换为日期格式 df['Month'] = df['date'].dt.month # 增加一列表示月份 df['days_to_departure'] = df['days_to_departure'].astype(int) # 将天数转换为整数类型 sns.set(style='whitegrid') fig, ax = plt.subplots(figsize=(10, 10)) sns.heatmap(df.pivot_table(index='days_to_departure', columns='date', values='lowest_price'), cmap='YlOrRd', ax=ax) ax.set_title('Flight Price Heatmap') ax.set_xlabel('Date') ax.set_ylabel('Days to Departure') plt.show()上述代码生成的热力图中将横轴的日期格式改为YYYY- MM- DD的形式
可以通过在 `pivot_table` 中设置 `aggfunc` 参数为一个 lambda 函数来实现:
```python
sns.heatmap(df.pivot_table(index='days_to_departure', columns='date', values='lowest_price', aggfunc=lambda x: x), cmap='YlOrRd', ax=ax)
```
然后,可以在 `ax.set_xticklabels` 中设置日期的格式来修改横轴的日期显示格式:
```python
ax.set_xticklabels([x.strftime('%Y-%m-%d') for x in df['date'].unique()], rotation=90)
```
完整代码如下:
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('data(北深).csv')
df['date'] = pd.to_datetime(df['date'])
df['Month'] = df['date'].dt.month
df['days_to_departure'] = df['days_to_departure'].astype(int)
sns.set(style='whitegrid')
fig, ax = plt.subplots(figsize=(10, 10))
sns.heatmap(df.pivot_table(index='days_to_departure', columns='date', values='lowest_price', aggfunc=lambda x: x), cmap='YlOrRd', ax=ax)
ax.set_title('Flight Price Heatmap')
ax.set_xlabel('Date')
ax.set_ylabel('Days to Departure')
ax.set_xticklabels([x.strftime('%Y-%m-%d') for x in df['date'].unique()], rotation=90)
plt.show()
```
解释import pandas as pd df = pd.read_csv('S12_wearther_central_park.csv') df['DATE'] = pd.to_datetime(df['DATE']) df.set_index('DATE', inplace=True) x = input() year_df = df.loc[str(x), ['PRCP', 'TMIN', 'TMAX']] rainy_days = year_df[year_df['PRCP'] > 1.3] print(rainy_days)
Certainly! Let me explain what each line of the code does:
```python
import pandas as pd
```
This line imports the Pandas library and assigns it the alias `pd`, which is commonly used in Python code.
```python
df = pd.read_csv('S12_wearther_central_park.csv')
```
This line reads the CSV file 'S12_wearther_central_park.csv' into a Pandas DataFrame called `df`. The data in the CSV file is assumed to be comma-separated.
```python
df['DATE'] = pd.to_datetime(df['DATE'])
```
This line converts the 'DATE' column of the DataFrame to a Pandas datetime object. This allows us to perform various operations on the date, such as filtering by year or month.
```python
df.set_index('DATE', inplace=True)
```
This line sets the 'DATE' column as the index of the DataFrame. This is useful for quickly accessing data based on the date.
```python
x = input()
```
This line prompts the user for input and assigns it to the variable `x`. This input is assumed to be a year in the format of a string, e.g. '2010'.
```python
year_df = df.loc[str(x), ['PRCP', 'TMIN', 'TMAX']]
```
This line creates a new DataFrame called `year_df` that contains the precipitation, minimum temperature, and maximum temperature data for the year specified by the user input. The `.loc` method is used to slice the DataFrame by the year, and the square brackets are used to select the columns of interest.
```python
rainy_days = year_df[year_df['PRCP'] > 1.3]
```
This line creates a new DataFrame called `rainy_days` that contains only the rows of `year_df` where the precipitation value is greater than 1.3 inches. This is done by using boolean indexing and comparing the 'PRCP' column to the value 1.3.
```python
print(rainy_days)
```
This line prints the `rainy_days` DataFrame to the console. This DataFrame contains the date, precipitation, minimum temperature, and maximum temperature for the days where precipitation was greater than 1.3 inches.