dataframe.py

#!/usr/local/bin/python3.9

import pandas as pd
import matplotlib.pyplot as plt


def df_stats(DataFrame):

    min = df["WindSpeed"].min()
    max = df["WindSpeed"].max()
    mean = df["WindSpeed"].mean()
    median = df["WindSpeed"].median()
    std = df["WindSpeed"].std()
    var = df["WindSpeed"].var()
    smode = df["WindSpeed"].mode()
    mode = ""
    for i in range(len(smode)):
        mode += str(moda[i])
    
    print("row x col:\t" + str(df.shape[0]) + " x " + str(df.shape[1]))
    print("MIN:\t" + str(df["WindSpeed"].min()) + " m/s")
    print("MAX:\t" + str(df["WindSpeed"].max()) + " m/s")
    print("MODE:\t\t", mode)
    print("MEAN:\t\t" + str(round(df["WindSpeed"].mean(), 2)))
    print("MEDIAN:\t" + str(df["WindSpeed"].median()) + "\n")
    print("STD:\t" + str(df["WindSpeed"].std()))
    print("VAR:\t" + str(df["WindSpeed"].var()) + "\n")

    return


def set_plt(plt):
    df["WindSpeed"].plot(kind = "hist", x = "WindSpeed", xticks = (range(0, 80, 2)), yticks = (range(0, 45000, 1000)), grid = True) # not null rows
    df["WindSpeed"].plot(kind = "hist", x = "WindSpeed", xticks = (range(0, 80, 2)), yticks = (range(0, 120000, 10000)), grid = True) # null rows
    df.plot(kind = "hist", x = "DateTime", y = "WindSpeed", yticks = (range(0, 100, 20)), grid = True)
    plt.title("FREQUENCIES GRAPHIC (null windspeed rows)")
    plt.xlabel("Windpeed")
    plt.ylabel("Frequency")
    plt.show()

    return

### DateTime, Velocity

df = pd.read_csv("anemometer_data.csv") # DateFrame with NULL windspeed rows
#df = df.loc[(df["WindSpeed"] != 0)] # DataFrame with NOT NULL windspeed rows

#df_stats(df)

df["WindSpeed"].plot(kind = "hist", x = "WindSpeed", xticks = (range(0, 90, 2)), yticks = (range(0, 140000, 2000)), grid = True) # null rows
#df["WindSpeed"].plot(kind = "hist", x = "WindSpeed", xticks = (range(0, 90, 2)), yticks = (range(0, 55000, 1500)), grid = True) # not null rows

plt.title("FREQUENCIES GRAPHIC (null windspeed rows)")
plt.xlabel("Windspeed")
plt.ylabel("Frequency")


plt.show()