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import random
random.random()

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import random

random.seed(0)
random.randint(0,9)

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import random
random_number = random.randint(0,9)
clients.loc[clients['client_id'] == random_number]

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import pandas as pd
clients = pd.DataFrame()
clients['client_id'] = [0,1,2,3,4,5,6,7,8,9]
clients['client_name'] = ["Mobili Ltd.","Tymy Ltd.", "Lukas Ltd.","Brod Ltd.", 
"Missyda Ltd.", "Abiti Ltd.", "Bomy Ltd." , "Citiwi Ltd.", "Dolphy Ltd.", "Doper Ltd."]

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Basic candlestick graph created with Python engine:

```{python,warning=FALSE}
import pandas as pd
import matplotlib.pyplot as plt
import datetime
from mpl_finance import candlestick_ohlc
import matplotlib.dates as mdates

ax = plt.subplot()

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Now let's work with Python pandas data frame inside R :

```{python}
import matplotlib.dates as mdates
py_data_frame = r.data


py_data_frame['Date']=py_data_frame['datetime'].map(mdates.datestr2num)

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Let's prepare the data with R:

```{r data_load}
library(reticulate)
data = read.csv("15m.csv",stringsAsFactors = FALSE) 
colnames(data)[1]<- "datetime"

head(data)
```

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Basic candlestick graph created with R engine:

```{r visualize_python_data}
r_data_frame %>%
  plot_ly(x = ~datetime, type="candlestick",
          open = ~Open, close = ~Close,
          high = ~High, low = ~Low) 

```

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Now let's work with Python pandas data frame inside R :
```{r check_python_data,include=FALSE}
library(reticulate)
library(plotly)
r_data_frame <- py$data
head(r_data_frame)
```

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Let's prepare the data with Python:

```{python data_load}
import pandas as pd
data = pd.read_csv("15m.csv") 
data.rename(columns={"Unnamed: 0": "datetime"},inplace=True)
    
data.head()
```