Pandas datetime
import pandas as pd import numpy as np from datetime import timedelta data = { "Date": [ "1965-05-08", "1975-08-02", "1981-10-25", "1994-11-27", "2002-10-22", "2015-06-12", "2019-08-30", "2021-11-23" ], "City": [ "Jacksonville", "Jacksonville", "Orlando", "Gainesville", "Sunrise", "Orlando", "Miami Gardens", "Hollywood" ], "Venue": [ "Jacksonville Coliseum", "Gator Bowl Stadium", "Orlando Stadium (Tangerine Bowl)", "Florida Field (Ben Hill Griffin Stadium)", "Office Depot Center", "Orlando Citrus Bowl (Camping World Stadium)", "Hard Rock Stadium", "Hard Rock Live at Seminole Hard Rock Hotel & Casino" ], "Estimated Capacity": [ 10000, # Approximate capacity for Jacksonville Coliseum 72000, # Approximate capacity for Gator Bowl Stadium 60000, 88000, 20000, 47225, 65326, 7000 ] } df = pd.DataFrame(data) df.head(10) 
df['Date'] = pd.to_datetime(df['Date'], errors='coerce') df2 = df.copy() df['year'] = df['Date'].dt.year df['month'] = df['Date'].dt.month df['dayofweek_int'] = df['Date'].dt.dayofweek df['dayofweek_name'] = df['Date'].dt.day_name() df 
df['Date'].min() 
df['Date'].max() 
df.loc[df['Date'] > '2020-01-01'] 
df.loc[(df['Date'] > '2020-01-01') & (df['Date'].dt.month == 11)] 
df['month_before_show'] = df['Date'] - pd.DateOffset(months=1) df.head(3) 
df['days_since_event'] = (pd.Timestamp('2025-04-17') - df['Date']).dt.days df.sort_values('Date') 
df.sort_values('Date', ascending=False) 
new_row = pd.DataFrame([{ 'Date': pd.NaT, 'City': 'Jacksonville', 'Venue': 'Metlife', 'Estimated Capacity': 70000 }]) df2 = pd.concat([df2, new_row], ignore_index=True) df2['Date'].isna() 
df2['Date'] = df2['Date'].fillna(pd.Timestamp('2019-07-19')) new_row_2 = pd.DataFrame([{ 'Date': pd.NaT, 'City': 'Plant City', 'Venue': 'Strawberry Festival Stadium', 'Estimated Capacity': 3000 }]) df2 = pd.concat([df2, new_row_2], ignore_index=True) df2.tail() 
df2.dropna(subset=['Date'], inplace=True) df2.tail() 
df['previous_concert'] = df['Date'].shift(1) df['next_concert'] = df['Date'].shift(-1) df2['Rolling_3_Event_Mean'] = df2['Estimated Capacity'].rolling(window=3).mean() df2['Expanding_Mean'] = df2['Estimated Capacity'].expanding().mean() df2.set_index('Date', inplace=True) df2 
df2.sort_index(inplace=True) df2['Estimated Capacity'].resample('10YS').mean() # monthly average 
df2['Estimated Capacity'].resample('10YS').agg({'mean', 'max', 'min','sum'}) 
df2.loc['2019'] 
df2.loc['2019-07'] 
df2.loc['2019-07-19'] 
df2.loc['2010-01-01':'2025-01-01'] 
df2.loc[(df2.index >= '2010-01-01') & (df2.index <= '2025-01-01')] 
df2['year'] = df2.index.year df2['month'] = df2.index.month df2['day of week'] = df2.index.dayofweek df2['day name'] = df2.index.day_name() data2 = { "Train Number": ["T123", "T456", "T789", "T101", "T202", "T303"], "Departure Time": ["08:00", "09:30", "11:00", "13:00", "15:30", "18:00"], "Destination": ["Zermatt", "Paris", "Zermatt", "Paris", "Zermatt", "Paris"], "Platform": [3, 5, 2, 6, 1, 4], "Status": ["On Time", "Delayed", "On Time", "On Time", "Delayed", "On Time"] } df_train_schedule = pd.DataFrame(data2) df_train_schedule 
today = pd.Timestamp.now().date() df_train_schedule["Departure Time"] = pd.to_datetime(df_train_schedule["Departure Time"].apply(lambda t: f"{today} {t}")) df_train_schedule["Departure Time"] = df_train_schedule["Departure Time"].dt.tz_localize("Europe/Zurich") df_train_schedule df_train_schedule['Departure Time EST'] = df_train_schedule['Departure Time'].dt.tz_convert('US/Eastern') df_train_schedule 
df_train_schedule["Next Departure"] = df_train_schedule.sort_values("Departure Time").groupby("Destination")["Departure Time"].shift(-1) df_train_schedule 
df_train_schedule['next_train_time_diff'] = df_train_schedule['Next Departure'] - df_train_schedule['Departure Time'] df_train_schedule 
df_train_schedule["Train Arrival Time"] = df_train_schedule["Departure Time"] + timedelta(hours=3) df_train_schedule 
filtered_train_times = df_train_schedule[ (df_train_schedule["Train Arrival Time"].dt.time >= pd.to_datetime("12:00").time()) & (df_train_schedule["Train Arrival Time"].dt.time <= pd.to_datetime("17:00").time()) ] filtered_train_times 
pd.date_range(start='2024-01-01', end='2024-01-10') 
pd.date_range(start='2024-01-01', periods=6, freq='ME') 
data = { 'date': [ '2025-04-01', '2025-04-02', '2025-04-02', # duplicate on 2nd '2025-04-04', '2025-04-06' # missing 3rd and 5th ], 'train': ['TGV 101', 'TGV 102', 'TGV 103', 'TGV 104', 'TGV 105'] } df3 = pd.DataFrame(data) df3['date'] = pd.to_datetime(df3['date']) duplicates = df3[df3['date'].duplicated()] duplicates 
data = { 'date': [ '2025-04-01', '2025-04-02', '2025-04-04', '2025-04-06', # missing 3rd, 5th '2025-04-07', '2025-04-09', # missing 8th '2025-04-10', '2025-04-11', '2025-04-12' ], 'train': [ 'TGV 101', 'TGV 103', 'TGV 104', 'TGV 105', 'TGV 106', 'TGV 107', 'TGV 108', 'TGV 109', 'TGV 110' ] } df4 = pd.DataFrame(data) df4['date'] = pd.to_datetime(df4['date']) df4 = df4.set_index(['date']) df_full = df4.asfreq('D', fill_value='No departures') df_full 
idx = pd.date_range('2023-01-01', periods=4, freq='ME') ts = pd.Series([10, 20, 30, 40], index=idx) ts_daily = ts.asfreq('D') ts_daily.head() 
data = { 'date': [ '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-06' # missing 5th ], 'train': ['TGV 101', 'TGV 102', np.nan, 'TGV 104', 'TGV 105'] } df7 = pd.DataFrame(data) df7 
df_filled_forward = df7.ffill() df_filled_forward 
df_filled_backward = df7.bfill() df_filled_backward 
full_range = pd.date_range(start=df3['date'].min(), end=df3['date'].max()) missing = full_range.difference(df3['date']) missing 
df = pd.DataFrame({'date_str': ['01/04/2025', '02/04/2025', '03/04/2025']}) df['date'] = pd.to_datetime(df['date_str'], format='%d/%m/%Y') df.head() 
df = pd.DataFrame({'date_str': ['04-01-2025', '04-02-2025']}) df['date'] = pd.to_datetime(df['date_str'], format='%m-%d-%Y') df.head() 
df = pd.DataFrame({'timestamp': ['01/04/2025 14:30:00', '02/04/2025 08:45:00']}) df['datetime'] = pd.to_datetime(df['timestamp'], format='%d/%m/%Y %H:%M:%S') df.head() 
df = pd.DataFrame({'date_str': ['01-Apr-2025', '02-Apr-2025']}) df['date'] = pd.to_datetime(df['date_str'], format='%d-%b-%Y') df.head() 
df = pd.DataFrame({'date_str': ['01 April 2025', '02 April 2025']}) df['date'] = pd.to_datetime(df['date_str'], format='%d %B %Y') df.head() 