Working With Date Column In Pandas Data Frame Using Python

Опубликовано: 01 Октябрь 2024
на канале: Parag Dhawan

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Working with date columns in pandas data frames can be useful for various tasks, including time-series analysis, data visualization, and data cleaning. Here are some common operations you can perform with date columns in pandas using Python:

Importing necessary libraries and loading data
import pandas as pd

Load data into a pandas data frame
df = pd.read_csv('data.csv')

Convert a column to a datetime format
df['date'] = pd.to_datetime(df['date'])
Extracting Information from Dates

Extracting year, month, and day from a date column
df['year'] = df['date'].dt.year
df['month'] = df['date'].dt.month
df['day'] = df['date'].dt.day

Extracting day of the week and weekday name from a date column
df['day_of_week'] = df['date'].dt.dayofweek
df['weekday_name'] = df['date'].dt.weekday_name

Filtering by Dates

Filtering by year
df[df['date'].dt.year == 2022]

Filtering by month
df[df['date'].dt.month == 2]

Filtering by day
df[df['date'].dt.day == 23]

Grouping by Dates
Grouping by year and aggregating by sum of sales
df.groupby(df['date'].dt.year)['sales'].sum()

Grouping by month and aggregating by mean of sales
df.groupby(df['date'].dt.month)['sales'].mean()

Grouping by day of the week and aggregating by count of sales
df.groupby(df['date'].dt.dayofweek)['sales'].count()

Shifting Dates
Shifting dates by a certain number of days
df['shifted_date'] = df['date'] + pd.Timedelta(days=7)

These are just a few examples of the many ways you can work with date columns in pandas data frames. Pandas has extensive functionality for working with dates and times, so there are many more operations you can perform to suit your specific needs.

1 Suppressing Warnings
2 Importing Pandas and NumPy
3 Generating data within a given data range
4 Extracting Year from Date Column
5 Extracting Month From date
6 Deriving month name abbreviate from month number using calendar module
7 Extracting Week Number From date
8 Extracting Quarter Number from Date
9 Deriving Quarter Name from Quarter Number
10 Extracting Week Day From date
11 Deriving Day Name from Week Day
12 Extracting Day of month From date
13 Extracting Hour from the date column
14 Extracting Minute from date column
15 Extraction Seconds from date column

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