diff --git a/APIs/APIs.py b/APIs/APIs.py
new file mode 100644
index 0000000..0b50b05
--- /dev/null
+++ b/APIs/APIs.py
@@ -0,0 +1,215 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[ ]:
+
+
+import glob, matplotlib, pprint, requests, math, seaborn
+import pandas as pd
+import numpy as np
+from collections import Counter
+from itertools import repeat
+import matplotlib.pyplot as plt
+get_ipython().run_line_magic('matplotlib', 'inline')
+
+dat = pd.read_csv() ## Please select the path to your data here
+
+
+
+def DeviceList(data, network=None, frequencyplan=None, manufacturer=None):
+    
+    # This lists identified devices
+    # Optional filters are 'network', 'frequencyplan', 'manufacturer'
+    
+    arg_list = [network, frequencyplan, manufacturer]
+    
+    # Applying the filters
+    for i in range(len(arg_list)):
+        if i == 0:
+            if network != None:
+                nwk = network
+            else:
+                nwk = data["network"]
+        elif i == 1:
+            if frequencyplan != None:
+                freqplan = frequencyplan
+            else:
+                freqplan = data["Freq_Plan"]
+        elif i == 2:
+            if manufacturer != None:
+                manf = manufacturer
+            else:
+                manf = data["deveui_manufacturer"]
+
+    # Output the result
+    result = data.loc[(data["Freq_Plan"] == freqplan) &
+                      (data["network"] == nwk) &
+                      (data["deveui_manufacturer"] == manf)]
+    result = result[["DevEUI or DevAddr", "Freq_Plan",
+                     "network", "deveui_manufacturer"]]
+    return(result.to_json(orient='records'))
+
+
+
+def ChannelOccupancy(data, datarate=None, frequencyplan=None):
+    
+    # This lists channel-wise packet distribution
+    # Optional filters can be 'datarate' and 'frequencyplan'
+    
+    arg_list = [datarate, frequencyplan]
+    
+    for i in range(len(arg_list)):
+        if i == 0:
+            if datarate != None:
+                dr = datarate
+            else:
+                dr = data["datarate"]
+        elif i == 1:
+            if frequencyplan != None:
+                freqplan = frequencyplan
+            else:
+                freqplan = data["Freq_Plan"]
+
+    # Applying the filters
+    filt = data.loc[(data["datarate"] == dr) & (data["Freq_Plan"] == freqplan)]
+    
+    # Cleaning the data format
+    filt = list(zip(filt["datarate"].str.strip(), filt["frequency"]/1000000))
+
+    # Counting packets
+    count = Counter(filt)
+    df = pd.DataFrame(count.values(),
+                      index=pd.MultiIndex.from_tuples(count.keys())).unstack(1)
+    result = df.fillna(0)[0].sort_index(axis=0)
+
+    # Converting to percentage
+    result = result.div(result.values.sum()/100)
+
+    return(result.round(2).to_json(orient='columns'))
+
+
+
+def RSSI(data, network=None, frequencyplan=None, manufacturer=None):
+    
+    # This shows the distribution of RSSI in percentage
+    # Optional filters are 'network', 'frequencyplan', 'manufacturer'
+    
+    arg_list = [network, frequencyplan, manufacturer]
+    
+    # Applying the filters
+    for i in range(len(arg_list)):
+        if i == 0:
+            if network != None:
+                nwk = network
+            else:
+                nwk = data["network"]
+        elif i == 1:
+            if frequencyplan != None:
+                freqplan = frequencyplan
+            else:
+                freqplan = data["Freq_Plan"]
+        elif i == 2:
+            if manufacturer != None:
+                manf = manufacturer
+            else:
+                manf = data["deveui_manufacturer"]
+
+    # Output the result
+    result = data.loc[(data["Freq_Plan"] == freqplan) &
+                      (data["network"] == nwk) &
+                      (data["deveui_manufacturer"] == manf)]
+    
+    # Create a new dataframe of the RSSI
+    rssi_data = zip(Counter(result["RSSI"]).keys(), Counter(result["RSSI"]).values())
+    result = pd.DataFrame(rssi_data, columns = ["RSSI", "Percentage"])
+    
+    # Converting to percentage
+    result["Percentage"] = np.round((result["Percentage"]/result["Percentage"].sum())*100, 3)
+    
+    #return(result)
+    return(result.to_json(orient='records'))
+
+
+
+def SNR(data, network=None, frequencyplan=None, manufacturer=None):
+    
+    # This shows the distribution of RSSI in percentage
+    # Optional filters are 'network', 'frequencyplan', 'manufacturer'
+    
+    arg_list = [network, frequencyplan, manufacturer]
+    
+    # Applying the filters
+    for i in range(len(arg_list)):
+        if i == 0:
+            if network != None:
+                nwk = network
+            else:
+                nwk = data["network"]
+        elif i == 1:
+            if frequencyplan != None:
+                freqplan = frequencyplan
+            else:
+                freqplan = data["Freq_Plan"]
+        elif i == 2:
+            if manufacturer != None:
+                manf = manufacturer
+            else:
+                manf = data["deveui_manufacturer"]
+
+    # Output the result
+    result = data.loc[(data["Freq_Plan"] == freqplan) &
+                      (data["network"] == nwk) &
+                      (data["deveui_manufacturer"] == manf)]
+    
+    # Create a new dataframe of the RSSI
+    snr_data = zip(Counter(result["SNR"]).keys(), Counter(result["SNR"]).values())
+    result = pd.DataFrame(snr_data, columns = ["SNR", "Percentage"])
+    
+    # Converting to percentage
+    result["Percentage"] = np.round((result["Percentage"]/result["Percentage"].sum())*100, 3)
+    
+    #return(result)
+    return(result.to_json(orient='records'))
+
+
+
+def NetworkList(data, frequencyplan=None, manufacturer=None):
+    
+    # This lists identified devices
+    # Optional filters are 'frequencyplan', 'manufacturer'
+    
+    arg_list = [frequencyplan, manufacturer]
+    
+    # Applying the filters
+    for i in range(len(arg_list)):
+        if i == 0:
+            if frequencyplan != None:
+                freqplan = frequencyplan
+            else:
+                freqplan = data["Freq_Plan"]
+        elif i == 1:
+            if manufacturer != None:
+                manf = manufacturer
+            else:
+                manf = data["deveui_manufacturer"]
+    
+    result = data.loc[(data["Freq_Plan"] == freqplan) &
+                      (data["deveui_manufacturer"] == manf)]
+    
+    # Split it from the original dataframe by DevAddr or DevEUI
+    # Output the result
+    l_freq = []
+    freq_p = list(set(result["Freq_Plan"]))
+    if len(freq_p) > 1:
+        for a, b in result.groupby("Freq_Plan", sort=False):
+            l_freq.append(b.sort_values(by=["sec_diff"]))
+        for i in range(len(l_freq)):
+            print(freq_p[i])
+            result = l_freq[i].groupby("network").nunique()["DevEUI or DevAddr"]
+            print(result.to_json(orient='columns'))
+    
+    else:
+        print(freq_p[0])
+        result = l_freq[0].groupby("network").nunique()["DevEUI or DevAddr"]
+        print(result.to_json(orient='columns'))
+