I have corn yield data (Mg/ha) that I want to visualize. First, let’s upload the data.
import pandas as pd import requests from io import StringIO github="https://raw.githubusercontent.com/agronomy4future/raw_data_practice/main/grain_yield_map.csv" response=requests.get(github) df=pd.read_csv(StringIO(response.text)) df.head(5) Latitude Longitude GY 0 12.15725 -106.14035 16.248 1 12.15724 -106.13994 26.703 2 12.15724 -106.13954 16.569 3 12.15723 -106.13911 21.451 4 12.15722 -106.13871 19.107F
First, I’ll create yield distribution data.
<strong># to open library</strong>
import numpy as np
import pandas as pd
import scipy.stats as stats
import matplotlib.pyplot as plt
from pylab import rcParams
import seaborn as sns
<strong># to calculate mean and standard deviation</strong>
df_mean= np.mean(df["GY"])
df_std= np.std(df["GY"])
<strong># to calculate probability density function</strong>
df_pdf= stats.norm.pdf(df["GY"].sort_values(), df_mean, df_std)
<strong># to draw normal distribution (with histogram) gragh</strong>
plt.plot(df["GY"].sort_values(), df_pdf, color= "Black")
sns.histplot(data= df["GY"], color= "Black", bins= 70, stat= "probability", alpha= 0.3)
plt.xlim([0,50])
plt.ylim([0,0.1])
plt.xlabel("Grain yield (Mg/ha)", size= 15)
plt.ylabel("Frequency", size= 15)
plt.grid(True, alpha=0.3, linestyle="--")
plt.rcParams["figure.figsize"]= [7,5]
plt.rcParams["figure.dpi"]= 500
plt.show()
Now, we can see that the general grain yield varies from 10 to 30 Mg/ha, with some outliers. I’ll create a yield map to visualize this variation.
import matplotlib.pyplot as plt import numpy as np from scipy.interpolate import griddata <strong># Extract the data</strong> latitude = df['Latitude'] longitude = df['Longitude'] p_concentration = df['GY'] <strong># Define grid for interpolation</strong> grid_x, grid_y = np.mgrid[latitude.min():latitude.max():100j, longitude.min():longitude.max():100j] <strong># Interpolate the data to a grid</strong> grid_z = griddata((latitude, longitude), p_concentration, (grid_x, grid_y), method='cubic') <strong># Define the levels for a simpler contour plot</strong> levels = list(range(0, 50, 5)) <strong># Create the plot with the specified levels and remove axis numbers</strong> plt.figure(figsize=(10, 6)) contour = plt.contourf(grid_y, grid_x, grid_z, levels=levels, cmap='RdYlGn') plt.colorbar(contour, label='Corn Grain Yield (Mg/ha)') <strong># Add scatter points</strong> plt.scatter(longitude, latitude, c='black', s=7) <strong># axis</strong> plt.xticks([]) plt.yticks([]) plt.show()
<strong># Full code</strong> import pandas as pd import requests from io import StringIO import matplotlib.pyplot as plt import numpy as np from scipy.interpolate import griddata github="https://raw.githubusercontent.com/agronomy4future/raw_data_practice/main/grain_yield_map.csv" response=requests.get(github) df=pd.read_csv(StringIO(response.text)) latitude = df['Latitude'] longitude = df['Longitude'] p_concentration = df['GY'] grid_x, grid_y = np.mgrid[latitude.min():latitude.max():100j, longitude.min():longitude.max():100j] grid_z = griddata((latitude, longitude), p_concentration, (grid_x, grid_y), method='cubic') levels = list(range(0, 50, 5)) plt.figure(figsize=(10, 6)) contour = plt.contourf(grid_y, grid_x, grid_z, levels=levels, cmap='RdYlGn') plt.colorbar(contour, label='Corn Grain Yield (Mg/ha)') plt.scatter(longitude, latitude, c='black', s=7) plt.xticks([]) plt.yticks([]) plt.show()
If you have the ArcGIS program, you can create a more detailed GIS map.
