CODEWITHSUNDEEP
pythonpython-3.x
Hallwalker
Hallwalker

Reputation: 3

Comparing dates of different months

I would love some guidance how I can compare the same dates over different years. I have daily mean temperature data for all March days from 1997-2018 and my goal is to see the mean temperature of each day over my time period. My df is simple and the head and tail looks like the following:

IndexType = Datetime

Date temp


1997-03-01  6.00

1997-03-02  6.22

1997-03-03  6.03

1997-03-04  4.41

1997-03-05  5.29


Date temp

2018-03-27 -2.44

2018-03-28 -1.01

2018-03-29 -1.08

2018-03-30 -0.53

2018-03-31 -0.11

I imagine the goal could be either 1) a dataframe with days as an index and years as column or 2) a Series with days as index and and the average daily temperature of 1997-2018.

My code:

df = pd.read_csv(file, sep=';', skiprows=9, usecols=[0, 1, 2, 3], parse_dates=[['Datum', 'Tid (UTC)']], index_col=0)
print(df.head())

df.columns = ['temp']
df.index.names = ['Date']

df_mar = df.loc[df.index.month == 3]
df_mar = df_mar.resample('D').mean().round(2)

Upvotes: 0

Views: 273

Answers (1)

Matt W.
Matt W.

Reputation: 3722

You can use groupby to see lots of comparisons. Not sure if that's exactly what you're looking for?

Make sure your date column is a Timestamp.

import pandas as pd
df = df.reset_index(drop=False)
df['Date'] = pd.to_datetime(df['Date'])

I'll initialize a dataframe to practice on:

import datetime
import random
base = datetime.datetime.today()
date_list = [base - datetime.timedelta(days=x) for x in range(0, 100000)]
df = pd.DataFrame({'date':date_list, 'temp':[random.randint(-30, 100) for x in range(100000)]})
march = df[df['date'].dt.month == 3]

g = march.groupby(march['date'].dt.day).agg({'temp':['max', 'min', 'mean']})

alternatively you can do this across your whole dataframe, not just march. 

df.groupby(df['date'].dt.month).agg({'temp':['max', 'min', 'mean', 'nunique']})
     temp                       
      max min       mean nunique
date                            
1     100 -30  34.999765     131
2     100 -30  35.167485     131
3     100 -30  35.660215     131
4     100 -30  34.436264     131
5     100 -30  35.424371     131
6     100 -30  35.086253     131
7     100 -30  35.188133     131
8     100 -30  34.772781     131
9     100 -30  34.839173     131
10    100 -30  35.248528     131
11    100 -30  34.666302     131
12    100 -30  34.575583     131

Upvotes: 1

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