Harold Nelson
11/1/2020
In Cocalc, create a folder called “Pandas Notes”.
Download the file OAW2.csv from Moodle. Then upload it to the folder you just created in Cocalc.
Create a Jupyter notebook in the same folder to do the work below.
The data is from the weather station at the Olympia Airport.
Make numpy available as np. Make pandas available as pd.
Get the Data.
Using pd.read_csv create the dataframe OAW2 from the csv file. Set the row number to index 0.
What is it?
Use the method info() to get some basic information about the dataframe.
## <class 'pandas.core.frame.DataFrame'>
## Int64Index: 28708 entries, 1 to 28708
## Data columns (total 8 columns):
## # Column Non-Null Count Dtype
## --- ------ -------------- -----
## 0 PRCP 28708 non-null float64
## 1 TMAX 28708 non-null float64
## 2 TMIN 28708 non-null float64
## 3 yr 28708 non-null int64
## 4 mo 28708 non-null int64
## 5 dy 28708 non-null int64
## 6 warmth 28708 non-null object
## 7 wetness 28708 non-null object
## dtypes: float64(3), int64(3), object(2)
## memory usage: 2.0+ MB
Get some statistical information on the contents.
Use the method describe().
## PRCP TMAX ... mo dy
## count 28708.000000 28708.000000 ... 28708.000000 28708.00000
## mean 0.136342 60.537656 ... 6.540999 15.73530
## std 0.300729 13.683659 ... 3.446058 8.80051
## min 0.000000 17.960000 ... 1.000000 1.00000
## 25% 0.000000 50.000000 ... 4.000000 8.00000
## 50% 0.000000 59.000000 ... 7.000000 16.00000
## 75% 0.141732 71.060000 ... 10.000000 23.00000
## max 4.818898 104.000000 ... 12.000000 31.00000
##
## [8 rows x 6 columns]
Use the methods head() and tail() to see the beginning and end of the dataframe.
## Beginning
## PRCP TMAX TMIN yr mo dy warmth wetness
## 1 0.000000 66.02 50.00 1941 5 13 Warm Dry
## 2 0.000000 62.96 46.94 1941 5 14 Warm Dry
## 3 0.299213 57.92 44.06 1941 5 15 Cold Really Wet
## 4 1.078740 55.04 44.96 1941 5 16 Cold Really Wet
## 5 0.059055 57.02 46.04 1941 5 17 Cold Damp
## End
## PRCP TMAX TMIN yr mo dy warmth wetness
## 28704 0.011811 44.06 35.06 2019 12 27 Cold Damp
## 28705 0.019685 44.06 33.08 2019 12 28 Cold Damp
## 28706 0.039370 46.94 35.96 2019 12 29 Cold Damp
## 28707 0.000000 46.94 42.08 2019 12 30 Cold Dry
## 28708 1.559055 51.98 44.06 2019 12 31 Cold Really Wet