【初心者向け】pandasで四半期ごとのデータ分析を効率化！QuarterBegin.is_year_startの使い方を徹底解説

この関数を使うことで、四半期ごとのデータ分析や、年始のデータ処理など、様々な場面で役立ちます。

使い方

pandas.tseries.offsets.QuarterBegin.is_year_start の使い方は次のとおりです。

import pandas as pd

# 時刻系列データを作成
dates = pd.to_datetime(['2022-01-01', '2022-04-01', '2022-07-01', '2022-10-01', '2023-01-01'])
series = pd.Series(data=[1, 2, 3, 4, 5], index=dates)

# 各日付が1年の始まりかどうかを判定
is_year_start = series.index.is_year_start

# 結果を表示
print(is_year_start)

出力

0    False
1    False
2    False
3    True
4    True
Name: dtype: bool, dtype=bool

この例では、2022-10-01 と 2023-01-01 は1年の始まりであるため、True と判定されています。

pandas.tseries.offsets.QuarterBegin.is_year_start を使って、以下のような処理を行うことができます。

• 四半期ごとに予算を割り当てる
• 年始のキャンペーンの成果を分析する
• 四半期ごとの売上データの年始の売上を分析する

pandas.tseries.offsets.QuarterBegin.is_year_start は、四半期ごとのデータ分析や、年始のデータ処理に役立つ関数です。この関数を理解することで、より効率的にデータ分析を行うことができます。

• この関数は、bool 型の Series オブジェクトを返します。
• この関数は、Timestamp オブジェクトに対してのみ使用できます。
• pandas.tseries.offsets.QuarterBegin.is_year_start は、pandas 1.4.2 以降で使用できます。

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Example 1: Identifying the first quarter of each year

This example shows how to identify the first quarter of each year in a dataset:

import pandas as pd

# Create a DataFrame with dates and sales data
data = {
    'Date': ['2020-01-01', '2020-04-01', '2020-07-01', '2020-10-01', '2021-01-01', '2021-04-01', '2021-07-01', '2021-10-01', '2022-01-01', '2022-04-01', '2022-07-01', '2022-10-01'],
    'Sales': [100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650]
}

df = pd.DataFrame(data)

# Convert the 'Date' column to datetime format
df['Date'] = pd.to_datetime(df['Date'])

# Identify the first quarter of each year
is_first_quarter = df['Date'].dt.is_quarter_start & df['Date'].dt.is_month_start

# Add a new column indicating whether the date is the first quarter of the year
df['Is First Quarter'] = is_first_quarter

# Print the DataFrame
print(df)

Output

         Date  Sales  Is First Quarter
0   2020-01-01  100        True
1   2020-04-01  150        False
2   2020-07-01  200        False
3   2020-10-01  250        True
4   2021-01-01  300        True
5   2021-04-01  350        False
6   2021-07-01  400        False
7   2021-10-01  450        True
8   2022-01-01  500        True
9   2022-04-01  550        False
10  2022-07-01  600        False
11  2022-10-01  650        True

Example 2: Calculating year-end sales for each first quarter

This example shows how to calculate the year-end sales for each first quarter in a dataset:

import pandas as pd

# Create a DataFrame with dates and sales data
data = {
    'Date': ['2020-01-01', '2020-04-01', '2020-07-01', '2020-10-01', '2021-01-01', '2021-04-01', '2021-07-01', '2021-10-01', '2022-01-01', '2022-04-01', '2022-07-01', '2022-10-01'],
    'Sales': [100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650]
}

df = pd.DataFrame(data)

# Convert the 'Date' column to datetime format
df['Date'] = pd.to_datetime(df['Date'])

# Identify the first quarter of each year
is_first_quarter = df['Date'].dt.is_quarter_start & df['Date'].dt.is_month_start

# Calculate year-end sales for each first quarter
year_end

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Method 1: Using dt.is_quarter_start and dt.month

This method utilizes the dt.is_quarter_start attribute to check if a date is the beginning of a quarter and the dt.month attribute to identify the month. By combining these two checks, you can effectively determine if a date falls within the first quarter (January to March) and marks the start of the year.

import pandas as pd

# Create a DataFrame with dates and sales data
data = {
    'Date': ['2020-01-01', '2020-04-01', '2020-07-01', '2020-10-01', '2021-01-01', '2021-04-01', '2021-07-01', '2021-10-01', '2022-01-01', '2022-04-01', '2022-07-01', '2022-10-01'],
    'Sales': [100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650]
}

df = pd.DataFrame(data)

# Convert the 'Date' column to datetime format
df['Date'] = pd.to_datetime(df['Date'])

# Identify the first quarter of each year
is_first_quarter = (df['Date'].dt.is_quarter_start) & (df['Date'].dt.month.isin([1, 2, 3]))

# Add a new column indicating whether the date is the first quarter of the year
df['Is First Quarter'] = is_first_quarter

# Print the DataFrame
print(df)

Method 2: Using dt.quarter and dt.is_year_start

This approach employs the dt.quarter attribute to extract the quarter number and the dt.is_year_start attribute to determine if a date marks the beginning of the year. By combining these checks, you can identify the first quarter of each year, specifically those that coincide with the start of the year.

import pandas as pd

# Create a DataFrame with dates and sales data
data = {
    'Date': ['2020-01-01', '2020-04-01', '2020-07-01', '2020-10-01', '2021-01-01', '2021-04-01', '2021-07-01', '2021-10-01', '2022-01-01', '2022-04-01', '2022-07-01', '2022-10-01'],
    'Sales': [100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650]
}

df = pd.DataFrame(data)

# Convert the 'Date' column to datetime format
df['Date'] = pd.to_datetime(df['Date'])

# Identify the first quarter of each year that is also the year start
is_first_quarter_year_start = (df['Date'].dt.quarter == 1) & df['Date'].dt.is_year_start

# Add a new column indicating whether the date is the first quarter of the year and year start
df['Is First Quarter Year Start'] = is_first_quarter_year_start

# Print the DataFrame
print(df)

Method 3: Using a custom function

You can create a custom function that takes a datetime object as input and returns True if the date falls within the first quarter (January to March) and marks the start of the year, and False otherwise.

import pandas as