df = pd.read_csv('./data/pollution.csv') print(len(df)) df.head(10) print(f.year.unique())

这段代码使用 pandas 库读取了一个名为 pollution.csv 的文件，并将其存储为一个 DataFrame 对象 df。接着，代码输出了 df 的行数，即数据集的大小，以及前10行数据。最后一行代码输出了数据集中所有年份的唯一值（即不重复的年份列表）。

需要注意的是，这段代码中的文件路径 './data/pollution.csv' 可能需要根据实际情况进行修改，以确保能够正确读取数据文件。

相关问题

import pandas as pd df = pd.read_csv('property-data.csv') new_df = df.dropna() print(new_df.to_st...

It seems like the code is incomplete. The last line of the code is cut off. However, based on what is provided, the code reads a CSV file called 'property-data.csv' into a pandas DataFrame called 'df'. Then, any rows with missing data are dropped using the 'dropna()' method and the resulting DataFrame is stored in a new variable called 'new_df'. Finally, the 'to_st...' method is called on 'new_df', but it is unclear what the full method call is supposed to be.

train_df = pd.read_csv('./data/train.csv', index_col = 0) test_df = pd.read_csv('./data/test.csv', index_col = 0) print("Number of rows: ", train_df.shape[0]) print("Number of columns: ", train_df.shape[1]) display(train_df) train_df.info() print("Number of rows: ", test_df.shape[0]) print("Number of columns: ", test_df.shape[1]) display(test_df) test_df.info() train_df.describe() # Check for NaN values and the number of unique values print("Are there any NaN values: ", train_df.isnull().values.any()) print(train_df.nunique()) # Count NaN values in each column nan_counts = train_df.isna().sum() # Print the counts print(nan_counts)

这段代码是用来读取两个csv文件，并对它们进行一些数据分析的。具体来说，它首先读取了一个名为train.csv的文件和一个名为test.csv的文件，并打印了它们的行数和列数。然后，它显示了train_df和test_df的内容和信息，包括每个列的数据类型和非空值的数量。接下来，它使用describe()函数对train_df进行描述统计，包括平均值、标准差、最小值、最大值等。最后，它检查train_df中是否有NaN值，并计算每列中的NaN值的数量，并打印出来。