report_automation_part_1.py

### SERP Presence ###

### Load SEMRush Credentials and URLs for API call.
api_key = 'api key goes here'
service_url = 'https://api.semrush.com'
client_url = 'https://www.clientsite.com/'

### Various time manipulations for each part of the report ###
today = datetime.date.today()
first = today.replace(day=1)
lastMonth = first - datetime.timedelta(days=1)
previous_month = lastMonth.strftime("%m_%Y")
date_main = lastMonth.strftime("%Y-%m")
date_breakdown = monthrange(int(lastMonth.strftime("%Y")), int(lastMonth.strftime("%m")))
day = date_breakdown[1]

#for search console
search_console_date = date_main + "-01"

#for backlinks
time_check = lastMonth.strftime("%Y-%m")

def build_urls(date_main):
    params = {
        "?type": "domain_organic",
        'key': api_key,
        'domain': client_url,
        'export_columns': "Ph,Po,Nq,Vu",
        'database': 'us',
        'display_date': date_main,
        'display_limit': '10000'
        }
    data = urllib.parse.urlencode(params, doseq=True)
    main_call = urllib.parse.urljoin(service_url, data)
    main_call = main_call.replace(r'%3F', r'?')
    return main_call

def parse_response(call_data):
        results = []
        data = call_data.decode('unicode_escape')
        lines = data.split('\r\n')
        lines = list(filter(bool, lines))
        columns = lines[0].split(';')

for line in lines[1:]:
            result = {}
            for i, datum in enumerate(line.split(';')):
                result[columns[i]] = datum.strip('"\n\r\t')
            results.append(result)
return results

def API_Call(url):
    try:
        response = requests.get(url)
        read = response.content
        final = parse_response(call_data=read)
    except IndexError as e:
        final = {}
    return final

# API URL build/call and data compiling and filtering
frames = []
call = build_urls(date_main=date_main)
nums = []
data_set = API_Call(url=call)
df = pd.DataFrame(data_set)
df['Top'] = df['Position'].astype(int).between(0,3, inclusive=True)
df_1_to_3 = df[df.Top.astype(str).str.contains("True")]
df['Top'] = df['Position'].astype(int).between(4,10, inclusive=True)
df_4_to_10 = df[df.Top.astype(str).str.contains("True")]
df['Top'] = df['Position'].astype(int).between(11,20, inclusive=True)
df_11_to_20 = df[df.Top.astype(str).str.contains("True")]
tops = df_1_to_3.Keyword.value_counts().sum()
middles = df_4_to_10.Keyword.value_counts().sum()
bottoms = df_11_to_20.Keyword.value_counts().sum()
nums.append((tops, middles, bottoms))
df1 = pd.DataFrame(nums, columns=['Top 3', '4 Thru 10', '11 Thru 20'])
frames.append(df1)
    
final_search_presence = pd.concat(frames)
final_search_presence['Month'] = date_main
final_search_presence['Top 10'] = final_search_presence['Top 3']+final_search_presence['4 Thru 10']