Learning to use deepnote, part 1
Idea of this piece of code is to try the use of deepnote. It seems that it is possible, although not cheap to use this for my studies, probably even for my work as a teacher.
import pandas as pd #https://pandas.pydata.org/
import matplotlib.pyplot as plt #https://matplotlib.org/
import matplotlib.dates as mdates #https://matplotlib.org/
import numpy as np #http://www.numpy.org/
Temperature2021 = pd.read_csv("data2021.csv",dayfirst=True,sep=",",
header=0,decimal=b".",index_col=0,
parse_dates= [[0, 1, 2, 3]],usecols=[0,1,2,3,5])
Temperature2021.info()
plt.figure(figsize=(16,6))
plt.plot(Temperature2021,color='blue', marker='.',linestyle='-')
plt.title("Air temperature in 2021 in Utti airport station")
plt.ylabel("Temperature in [°C]")
plt.grid(True)
plt.show()
Temperature122021 = pd.read_csv("data122021.csv",dayfirst=True,sep=",",
header=0,decimal=b".",index_col=0,
parse_dates= [[0, 1, 2, 3]],usecols=[0,1,2,3,5])
Temperature122021.info()
plt.figure(figsize=(16,6))
plt.plot(Temperature122021,color='blue', marker='.',linestyle='-')
plt.title("Air temperature in december 2021 in Utti airport station")
plt.ylabel("Temperature in [°C]")
plt.grid(True)
plt.show()
Temperature1vk2022 = pd.read_csv("data1vk2022.csv",dayfirst=True,sep=",",
header=0,decimal=b".",index_col=0,
parse_dates= [[0, 1, 2, 3]],usecols=[0,1,2,3,5])
Temperature1vk2022.info()
plt.figure(figsize=(16,6))
plt.plot(Temperature1vk2022,color='blue', marker='.',linestyle='-')
plt.title("Air temperature in first week of 2022 in Utti airport station")
plt.ylabel("Temperature in [°C]")
plt.grid(True)
plt.show()
Temperature130122 = pd.read_csv("data130122.csv",dayfirst=True,sep=",",
header=0,decimal=b".",index_col=0,
parse_dates= [[0, 1, 2, 3]],usecols=[0,1,2,3,5])
Temperature130122.info()
plt.figure(figsize=(16,6))
plt.plot(Temperature130122,color='blue', marker='.',linestyle='-')
plt.title("Air temperature in 13.1.2022 in Utti airport station")
plt.ylabel("Temperature in [°C]")
plt.grid(True)
plt.show()
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 8761 entries, 2021-01-01 00:00:00 to 2022-01-01 00:00:00
Data columns (total 1 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Ilman lämpötila (degC) 8759 non-null float64
dtypes: float64(1)
memory usage: 136.9 KB
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 745 entries, 2021-12-01 00:00:00 to 2022-01-01 00:00:00
Data columns (total 1 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Ilman lämpötila (degC) 745 non-null float64
dtypes: float64(1)
memory usage: 11.6 KB
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 169 entries, 2022-01-03 00:00:00 to 2022-01-10 00:00:00
Data columns (total 1 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Ilman lämpötila (degC) 169 non-null float64
dtypes: float64(1)
memory usage: 2.6 KB
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 145 entries, 2022-01-13 00:00:00 to 2022-01-14 00:00:00
Data columns (total 1 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Ilman lämpötila (degC) 145 non-null float64
dtypes: float64(1)
memory usage: 2.3 KB