How to set limits of Y-axes in countplot?
$begingroup$
df in my program happens to be a dataframe with these columns :
df.columns
'''output : Index(['lat', 'lng', 'desc', 'zip', 'title', 'timeStamp', 'twp', 'addr', 'e',
'reason'],
dtype='object')'''
When I execute this piece of code:
sns.countplot(x = df['reason'], data=df)
# output is the plot below
but if i slightly tweak my code like this :
p = df['reason'].value_counts()
k = pd.DataFrame({'causes':p.index,'freq':p.values})
sns.countplot(x = k['causes'], data = k)
So essentially I just stored the 'reasons' column values and its frequencies as a series in p and then converted them to another dataframe k but this new countplot doesn't have the right range of Y-axis for the given values.
My doubts happen to be :
- Can we set of Y-axis in the second countplot in its appropriate limits
- Why the does second countplot differ from the first one when i just separated the specific column i wanted to graph and plotted it separately ?
python dataframe matplotlib seaborn
asked 2 days ago
Arnav DasArnav Das
1033
New contributor
Arnav Das is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
df in my program happens to be a dataframe with these columns :
df.columns
'''output : Index(['lat', 'lng', 'desc', 'zip', 'title', 'timeStamp', 'twp', 'addr', 'e',
'reason'],
dtype='object')'''
When I execute this piece of code:
sns.countplot(x = df['reason'], data=df)
# output is the plot below
but if i slightly tweak my code like this :
p = df['reason'].value_counts()
k = pd.DataFrame({'causes':p.index,'freq':p.values})
sns.countplot(x = k['causes'], data = k)
So essentially I just stored the 'reasons' column values and its frequencies as a series in p and then converted them to another dataframe k but this new countplot doesn't have the right range of Y-axis for the given values.
My doubts happen to be :
- Can we set of Y-axis in the second countplot in its appropriate limits
- Why the does second countplot differ from the first one when i just separated the specific column i wanted to graph and plotted it separately ?
python dataframe matplotlib seaborn
asked 2 days ago
Arnav DasArnav Das
1033
New contributor
Arnav Das is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
df in my program happens to be a dataframe with these columns :
df.columns
'''output : Index(['lat', 'lng', 'desc', 'zip', 'title', 'timeStamp', 'twp', 'addr', 'e',
'reason'],
dtype='object')'''
When I execute this piece of code:
sns.countplot(x = df['reason'], data=df)
# output is the plot below
but if i slightly tweak my code like this :
p = df['reason'].value_counts()
k = pd.DataFrame({'causes':p.index,'freq':p.values})
sns.countplot(x = k['causes'], data = k)
So essentially I just stored the 'reasons' column values and its frequencies as a series in p and then converted them to another dataframe k but this new countplot doesn't have the right range of Y-axis for the given values.
My doubts happen to be :
- Can we set of Y-axis in the second countplot in its appropriate limits
- Why the does second countplot differ from the first one when i just separated the specific column i wanted to graph and plotted it separately ?
python dataframe matplotlib seaborn
asked 2 days ago
Arnav DasArnav Das
1033
New contributor
Arnav Das is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
df in my program happens to be a dataframe with these columns :
df.columns
'''output : Index(['lat', 'lng', 'desc', 'zip', 'title', 'timeStamp', 'twp', 'addr', 'e',
'reason'],
dtype='object')'''
When I execute this piece of code:
sns.countplot(x = df['reason'], data=df)
# output is the plot below
but if i slightly tweak my code like this :
p = df['reason'].value_counts()
k = pd.DataFrame({'causes':p.index,'freq':p.values})
sns.countplot(x = k['causes'], data = k)
So essentially I just stored the 'reasons' column values and its frequencies as a series in p and then converted them to another dataframe k but this new countplot doesn't have the right range of Y-axis for the given values.
My doubts happen to be :
- Can we set of Y-axis in the second countplot in its appropriate limits
- Why the does second countplot differ from the first one when i just separated the specific column i wanted to graph and plotted it separately ?
python dataframe matplotlib seaborn
python dataframe matplotlib seaborn
asked 2 days ago
Arnav DasArnav Das
1033
New contributor
Arnav Das is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
Arnav DasArnav Das
1033
New contributor
Arnav Das is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
Arnav DasArnav Das
1033
New contributor
Arnav Das is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
Arnav DasArnav Das
1033
asked 2 days ago
Arnav DasArnav Das
1033
1033
New contributor
Arnav Das is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Arnav Das is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Arnav Das is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
1 Answer
1
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oldest
votes
$begingroup$
Countplot from seaborn will not work as you expect. When you calculate the frequencies, you want to plot the values in p.values as they appear. Countplot will take a dataframe where labels are not aggregated and then count each one of them, as it did in the first case.
So countplot will be appropriate for the case where your dataframe looks like:
index | reason |
0 EMS
1 EMS
2 Traffic
3 Fire
4 Fire
5 EMS
6 Traffic
...
In the second case you already have your frequencies:
index | reason |
EMS 10
Traffic 21
Fire 15
Then count plot will just count the lines and it will be one for each, that is why your plot looks like that.
To solve your problem you could just plot using .plot from pandas:
df['reason'].value_counts(normalize=True).plot(kind='bar')
Where the parameter normalize=True will show normalized frequencies instead of raw count values.
answered 2 days ago
Victor OliveiraVictor Oliveira
1807
$endgroup$
$begingroup$
wow I had never thought that way, so countplot counts the lines only
$endgroup$
– Arnav Das
2 days ago
1
$begingroup$
Yes, thats right. So in the second case you should use barplot only.
$endgroup$
– Victor Oliveira
2 days ago
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
Countplot from seaborn will not work as you expect. When you calculate the frequencies, you want to plot the values in p.values as they appear. Countplot will take a dataframe where labels are not aggregated and then count each one of them, as it did in the first case.
So countplot will be appropriate for the case where your dataframe looks like:
index | reason |
0 EMS
1 EMS
2 Traffic
3 Fire
4 Fire
5 EMS
6 Traffic
...
In the second case you already have your frequencies:
index | reason |
EMS 10
Traffic 21
Fire 15
Then count plot will just count the lines and it will be one for each, that is why your plot looks like that.
To solve your problem you could just plot using .plot from pandas:
df['reason'].value_counts(normalize=True).plot(kind='bar')
Where the parameter normalize=True will show normalized frequencies instead of raw count values.
answered 2 days ago
Victor OliveiraVictor Oliveira
1807
$endgroup$
$begingroup$
wow I had never thought that way, so countplot counts the lines only
$endgroup$
– Arnav Das
2 days ago
1
$begingroup$
Yes, thats right. So in the second case you should use barplot only.
$endgroup$
– Victor Oliveira
2 days ago
add a comment |
$begingroup$
Countplot from seaborn will not work as you expect. When you calculate the frequencies, you want to plot the values in p.values as they appear. Countplot will take a dataframe where labels are not aggregated and then count each one of them, as it did in the first case.
So countplot will be appropriate for the case where your dataframe looks like:
index | reason |
0 EMS
1 EMS
2 Traffic
3 Fire
4 Fire
5 EMS
6 Traffic
...
In the second case you already have your frequencies:
index | reason |
EMS 10
Traffic 21
Fire 15
Then count plot will just count the lines and it will be one for each, that is why your plot looks like that.
To solve your problem you could just plot using .plot from pandas:
df['reason'].value_counts(normalize=True).plot(kind='bar')
Where the parameter normalize=True will show normalized frequencies instead of raw count values.
answered 2 days ago
Victor OliveiraVictor Oliveira
1807
$endgroup$
$begingroup$
wow I had never thought that way, so countplot counts the lines only
$endgroup$
– Arnav Das
2 days ago
1
$begingroup$
Yes, thats right. So in the second case you should use barplot only.
$endgroup$
– Victor Oliveira
2 days ago
add a comment |
$begingroup$
Countplot from seaborn will not work as you expect. When you calculate the frequencies, you want to plot the values in p.values as they appear. Countplot will take a dataframe where labels are not aggregated and then count each one of them, as it did in the first case.
So countplot will be appropriate for the case where your dataframe looks like:
index | reason |
0 EMS
1 EMS
2 Traffic
3 Fire
4 Fire
5 EMS
6 Traffic
...
In the second case you already have your frequencies:
index | reason |
EMS 10
Traffic 21
Fire 15
Then count plot will just count the lines and it will be one for each, that is why your plot looks like that.
To solve your problem you could just plot using .plot from pandas:
df['reason'].value_counts(normalize=True).plot(kind='bar')
Where the parameter normalize=True will show normalized frequencies instead of raw count values.
answered 2 days ago
Victor OliveiraVictor Oliveira
1807
$endgroup$
Countplot from seaborn will not work as you expect. When you calculate the frequencies, you want to plot the values in p.values as they appear. Countplot will take a dataframe where labels are not aggregated and then count each one of them, as it did in the first case.
So countplot will be appropriate for the case where your dataframe looks like:
index | reason |
0 EMS
1 EMS
2 Traffic
3 Fire
4 Fire
5 EMS
6 Traffic
...
In the second case you already have your frequencies:
index | reason |
EMS 10
Traffic 21
Fire 15
Then count plot will just count the lines and it will be one for each, that is why your plot looks like that.
To solve your problem you could just plot using .plot from pandas:
df['reason'].value_counts(normalize=True).plot(kind='bar')
Where the parameter normalize=True will show normalized frequencies instead of raw count values.
answered 2 days ago
Victor OliveiraVictor Oliveira
1807
answered 2 days ago
Victor OliveiraVictor Oliveira
1807
answered 2 days ago
Victor OliveiraVictor Oliveira
1807
answered 2 days ago
Victor OliveiraVictor Oliveira
1807
1807
$begingroup$
wow I had never thought that way, so countplot counts the lines only
$endgroup$
– Arnav Das
2 days ago
1
$begingroup$
Yes, thats right. So in the second case you should use barplot only.
$endgroup$
– Victor Oliveira
2 days ago
add a comment |
$begingroup$
wow I had never thought that way, so countplot counts the lines only
$endgroup$
– Arnav Das
2 days ago
1
$begingroup$
Yes, thats right. So in the second case you should use barplot only.
$endgroup$
– Victor Oliveira
2 days ago
$begingroup$
wow I had never thought that way, so countplot counts the lines only
$endgroup$
– Arnav Das
2 days ago
$begingroup$
wow I had never thought that way, so countplot counts the lines only
$endgroup$
– Arnav Das
2 days ago
1
1
$begingroup$
Yes, thats right. So in the second case you should use barplot only.
$endgroup$
– Victor Oliveira
2 days ago
$begingroup$
Yes, thats right. So in the second case you should use barplot only.
$endgroup$
– Victor Oliveira
2 days ago
add a comment |
Arnav Das is a new contributor. Be nice, and check out our Code of Conduct.
Arnav Das is a new contributor. Be nice, and check out our Code of Conduct.
Arnav Das is a new contributor. Be nice, and check out our Code of Conduct.
Arnav Das is a new contributor. Be nice, and check out our Code of Conduct.
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