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Add Python syntax highlighting to README.md #604

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69 changes: 48 additions & 21 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -50,16 +50,19 @@ Only good until Google changes their backend again :-P. When that happens feel f

### Connect to Google

from pytrends.request import TrendReq
```python
from pytrends.request import TrendReq

pytrends = TrendReq(hl='en-US', tz=360)
pytrends = TrendReq(hl='en-US', tz=360)
```

or if you want to use proxies as you are blocked due to Google rate limit:

```python
from pytrends.request import TrendReq

from pytrends.request import TrendReq

pytrends = TrendReq(hl='en-US', tz=360, timeout=(10,25), proxies=['https://34.203.233.13:80',], retries=2, backoff_factor=0.1, requests_args={'verify':False})
pytrends = TrendReq(hl='en-US', tz=360, timeout=(10,25), proxies=['https://34.203.233.13:80',], retries=2, backoff_factor=0.1, requests_args={'verify':False})
```

* `timeout(connect, read)`
- See explantation on this on [requests docs](https://requests.readthedocs.io/en/master/user/advanced/#timeouts)
Expand Down Expand Up @@ -87,8 +90,10 @@ Note: the parameter `hl` specifies host language for accessing Google Trends.
Note: only https proxies will work, and you need to add the port number after the proxy ip address

### Build Payload
kw_list = ["Blockchain"]
pytrends.build_payload(kw_list, cat=0, timeframe='today 5-y', geo='', gprop='')
```python
kw_list = ["Blockchain"]
pytrends.build_payload(kw_list, cat=0, timeframe='today 5-y', geo='', gprop='')
```

Parameters

Expand Down Expand Up @@ -203,24 +208,30 @@ Many API methods use the following:

### Interest Over Time

pytrends.interest_over_time()
```python
pytrends.interest_over_time()
```

Returns pandas.Dataframe

<sub><sup>[back to top](#interest_over_time)</sub></sup>

### Multirange Interest Over Time

pytrends.build_payload(kw_list=['pizza', 'bagel'], timeframe=['2022-09-04 2022-09-10', '2022-09-18 2022-09-24']))
pytrends.multirange_interest_over_time()
```python
pytrends.build_payload(kw_list=['pizza', 'bagel'], timeframe=['2022-09-04 2022-09-10', '2022-09-18 2022-09-24']))
pytrends.multirange_interest_over_time()
```

Returns pandas.Dataframe. It includes the average in the first row.

<sub><sup>[back to top](#multirange_interest_over_time)</sub></sup>

### Historical Hourly Interest

pytrends.get_historical_interest(kw_list, year_start=2018, month_start=1, day_start=1, hour_start=0, year_end=2018, month_end=2, day_end=1, hour_end=0, cat=0, geo='', gprop='', sleep=0)
```python
pytrends.get_historical_interest(kw_list, year_start=2018, month_start=1, day_start=1, hour_start=0, year_end=2018, month_end=2, day_end=1, hour_end=0, cat=0, geo='', gprop='', sleep=0)
```

Parameters

Expand All @@ -243,7 +254,9 @@ Returns pandas.Dataframe

### Interest by Region

pytrends.interest_by_region(resolution='COUNTRY', inc_low_vol=True, inc_geo_code=False)
```python
pytrends.interest_by_region(resolution='COUNTRY', inc_low_vol=True, inc_geo_code=False)
```

Parameters

Expand All @@ -268,41 +281,51 @@ Returns pandas.DataFrame

### Related Topics

pytrends.related_topics()
```python
pytrends.related_topics()
```

Returns dictionary of pandas.DataFrames

<sub><sup>[back to top](#related_topics)</sub></sup>

### Related Queries

pytrends.related_queries()
```python
pytrends.related_queries()
```

Returns dictionary of pandas.DataFrames

<sub><sup>[back to top](#related_queries)</sub></sup>

### Trending Searches

pytrends.trending_searches(pn='united_states') # trending searches in real time for United States
pytrends.trending_searches(pn='japan') # Japan
```python
pytrends.trending_searches(pn='united_states') # trending searches in real time for United States
pytrends.trending_searches(pn='japan') # Japan
```

Returns pandas.DataFrame

<sub><sup>[back to top](#trending_searches)</sub></sup>

### Realtime Search Trends

pytrends.realtime_trending_searches(pn='US') # realtime search trends for United States
pytrends.realtime_trending_searches(pn='IN') # India
```python
pytrends.realtime_trending_searches(pn='US') # realtime search trends for United States
pytrends.realtime_trending_searches(pn='IN') # India
```

Returns pandas.DataFrame

<sub><sup>[back to top](#realtime-search-trends)</sub></sup>

### Top Charts

pytrends.top_charts(date, hl='en-US', tz=300, geo='GLOBAL')
```python
pytrends.top_charts(date, hl='en-US', tz=300, geo='GLOBAL')
```

Parameters

Expand All @@ -320,7 +343,9 @@ Returns pandas.DataFrame

### Suggestions

pytrends.suggestions(keyword)
```python
pytrends.suggestions(keyword)
```

Parameters

Expand All @@ -335,7 +360,9 @@ Returns dictionary

### Categories

pytrends.categories()
```python
pytrends.categories()
```

Returns dictionary

Expand Down