diff --git a/tiktok_downloader/hashtag_frequencies.py b/tiktok_downloader/hashtag_frequencies.py
new file mode 100644
index 0000000..3f9a07f
--- /dev/null
+++ b/tiktok_downloader/hashtag_frequencies.py
@@ -0,0 +1,122 @@
+import os, sys
+import csv, json
+import argparse
+import matplotlib.pyplot as plt
+from datetime import datetime
+from file_methods import check_file
+from global_data import IMAGES
+
+
+"""
+Plots the frequency of hashtags appearing in the set of given posts.
+"""
+
+
+
+def get_hashtags(obj):
+    if not obj:
+        print(f'ERROR: Empty item, no hashtags to be extracted.')
+        return
+    else:
+        hashtags = {}
+        tags = [ [tag['name'] for tag in ele['hashtags']] for ele in obj ]
+        tags = [ set(ele) for ele in tags ]
+        { tag: (1 if tag not in hashtags and not hashtags.update({tag: 1})
+            else hashtags[tag] + 1 and not hashtags.update({tag: hashtags[tag] + 1})) 
+            for ele in tags for tag in ele }
+        hashtags = sorted(hashtags.items(), key=lambda e: e[1], reverse=True)
+
+        return hashtags
+
+
+def get_occurrences(filename, n=1 , sort=True):
+    """
+    Takes the json file containing posts and returns a dictionary:
+    local variable occs = {
+        "total": total posts in the file,
+        top_n: [[top n hashtags ], [frequencies of corresponding hashtags]]
+    }
+    """
+    with open(filename) as f:
+        obj = json.load(f)
+        l = len(obj)
+        tags = get_hashtags(obj)
+        occs = {
+                "total": l,
+                "top_n": []
+                }
+        occs["top_n"] = [ [ ele[i] for ele in tags[0:n] ] for i in range(2)]
+        return occs
+
+
+def plot(n, occs, img_folder):
+    plt.scatter(occs["top_n"][0], occs["top_n"][1])
+    plt.tight_layout()
+    plt.xticks(rotation=45)
+    plt.title(f'Hashtag Distribution')
+    plt.xlabel(f'Top {n} hashtags from {occs["total"]} posts.')
+    plt.ylabel(f'Number of occurrences')
+    save_plot(img_folder)
+    plt.show(block=None)
+    return
+
+
+def print_occurrences(occs):
+    """
+    Prints the top n hashtags with their frequencies and the ratio of occurrences and total posts, all to the shell.
+    """
+    row_number = 0
+    total_posts = occs["total"]
+    print ("{:<8} {:<15} {:<15} {:<15}".format("Rank", 'Hashtag','Occurrences',f'Frequency (Occurrences/Total-Posts(total_posts))'))
+    for key,value in zip(occs["top_n"][0], occs["top_n"][1]):
+        ratio = value/total_posts 
+        print ("{:<8} {:<15} {:<15} {:<15}".format(row_number, key, value, ratio))
+        row_number += 1
+    return
+
+
+def save_plot(img_folder):
+    """
+    Saves the plot to a png file in the folder /data/imgs/
+    """
+    try:
+        now = datetime.now()
+        current_time = now.strftime("%Y_%m_%d_%H_%M_%S")
+        plt.savefig(f"{img_folder}/{current_time}.png")
+
+        return
+    except: raise
+
+
+
+if __name__ == "__main__":
+    """
+    Option "n" specifies how many hashtags does the user wants to plot.
+    "-d" option prints the hashtag frequencies on the shell
+    "-p" option plots the hashtag frequencies and saves as a png file in the folder /data/imgs/
+
+    The function get_occurances is triggered to compute and return the top n occurances and the hashtags.
+    """
+    img_folder = IMAGES
+    check_file(img_folder, "dir")
+    parser = argparse.ArgumentParser()
+    parser.add_argument("input_file", help="The json hashtag file name")
+    parser.add_argument("n", help="The number of top n occurrences", type=int)
+    parser.add_argument("-p", "--plot", help="Plot the occurrences", action="store_true")
+    parser.add_argument("-d", "--print", help="List top n hashtags", action="store_true")
+    args = parser.parse_args()
+    if args.input_file and args.n:
+        if args.n < 1:
+            print(f"Please make sure the number of top occurrences is a positive integer.")
+            sys.exit()
+
+        base = os.path.splitext(args.input_file)[0]
+        path = f"./{base}_sorted_hashtags.csv"
+        if args.plot:
+            occs = get_occurrences(args.input_file, args.n)
+            plot(args.n, occs, img_folder)
+        else:
+            occs = get_occurrences(args.input_file, args.n)
+            print_occurrences(occs)
+    else:
+        print(f'ERROR: either {args.input_file} or {args.n} or both contains error.')