您可以使用以下代码：

# convert "Precipitation" and "date" values to proper types
df['Precipitation'] = df['Precipitation'].astype(int)
df["date"] = pd.to_datetime(df["date"])

# find rows that have "Precipitation" > 2
df['Count']= df.apply(lambda x: x["Precipitation"] > 2, axis=1)

# group df by year and drop the "Precipitation" column
df.groupby(df['date'].dt.year).sum().drop(columns=['Precipitation'])

@Tatthew你也可以用query和Groupby.size来做这个。

import pandas as pd
df = pd.DataFrame({'Date': ['2000-01-01', '2000-01-03',
                            '2000-01-03', '2001-01-01',
                            '2001-01-02', '2001-01-03',
                            '2002-01-01', '2002-01-02',
                            '2002-01-03'],
                   'Precipitation': [1, 6, 5, 3, 1, 0,
                                     10, 8, 12]})
df = df.astype({'Date': datetime64})
above_threshold = df.query('Precipitation > 2')
above_threshold.groupby(above_threshold.Date.dt.year).size()

@Tatthew你可以用GroupBy.apply来做这个：

import pandas as pd
df = pd.DataFrame({'Date': ['2000-01-01', '2000-01-03',
                            '2000-01-03', '2001-01-01',
                            '2001-01-02', '2001-01-03',
                            '2002-01-01', '2002-01-02',
                            '2002-01-03'],
                   'Precipitation': [1, 6, 5, 3, 1, 0,
                                     10, 8, 12]})
df = df.astype({'Date': datetime64})
df.groupby(df.Date.dt.year).apply(lambda df: df.Precipitation[df.Precipitation > 2].count())