🔒 Graph issues matplotlib and pandas

[tawny pawn]

Hi, Basically at each X point i want to Have the mean Y graphed but im not entirely sure how to do that.

[stark dock]

@tawny pawn

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Hey @tawny pawn!

Please edit your message to use a code block

```py
print('Hello, world!')
```

This will result in the following:

print('Hello, world!')```

[tawny pawn]

def lineChartM(df2):   ## Makes the  Line graph
    for i in df2.index:
        if (df2.loc[i,'gender']) == ("female"): #Removes all the female 
            df2 = df2.drop(i)
    df2 = df2.round(decimals=0)              
    plt.figure(figsize=(10, 6))
    df2 =df2.sort_values(by= 'daily_social_media_hours',ascending=True)
    df2 =df2
    plt.plot(df2['daily_social_media_hours'],
             df2['stress_level'],
             marker='o',
             linestyle='-',
             label='Stress Level')
    plt.title("Stress level over daily social media hours in male teens")
    plt.xlabel("Daily social media hours")
    plt.ylabel("Stress levels")
    plt.legend()
    plt.xticks(rotation=45)
    plt.tight_layout()
    return convertGraphToImage()
df6 = pd.read_csv('TeenMentalHealthClean.csv')
def lineChartF(df6):   ## Makes the  Line graph
    for i in df6.index:
        if (df6.loc[i,'gender']) == ("male"):
            df6 = df6.drop(i)   #removes all the male values
    plt.figure(figsize=(10, 6))
    df6 = df6.round(decimals=0)
   
  

    df6 =df6.sort_values(by= 'daily_social_media_hours',ascending=True)
    
    plt.plot(df6['daily_social_media_hours'],
             df6['stress_level'],
             marker='o',
             linestyle='-',
             label='Stress Level')
    plt.title("Stress level over daily social media hours in female teens")
    plt.xlabel("Daily social media hours")
    plt.ylabel("Stress levels")
    plt.legend()
    plt.xticks(rotation=45)
    plt.tight_layout()
    return convertGraphToImage()

[sturdy falcon]

You need a groupby

[tawny pawn]

yeah i thought so but when i was trying a groupby.mean() it was giving me a str error

"TypeError: dtype 'str' does not support operation 'mean'"

[sturdy falcon]

You need filter the column on which you want to compute the mean

Something like

df.groupby("key_column")["target_column"].mean()

No sure about the syntax exactly but that's the idea

[tawny pawn]

df6 = df6.groupby("stress_levels")["daily_social_media_ours"].mean(0)
```?

Like this

[sturdy falcon]

do not assign it to df6, you'll overwrite the df

Unless you don't need it anymore

But I would say yes, try it

[barren valve]

group according to the X axis. For each X axis point (social media hours), you want to group the sample values (stress levels reported for that number of social media hours) to get their mean.

Incidentally,

    for i in df6.index:
        if (df6.loc[i,'gender']) == ("male"):
            df6 = df6.drop(i)   #removes all the male values

putting Pandas data into your own loop is a red flag. It will generally be much slower than letting Pandas handle the looping for you.
For this kind of filtering, see https://stackoverflow.com/questions/17071871

[tawny pawn]

Oh ty.

KeyError: 'daily_social_media_hours'
Currently upon grouping it i get this error depending on which is first in the group by

[barren valve]

well, that will require standard debugging techniques, and looking at the data you actually have.

[tawny pawn]

Ty I think i got it working

adding .reset_index() seems to have fixed it

Ty both so muchh

[sturdy falcon]

Nice

No problem

[stark dock]

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