CODEWITHSUNDEEP
pythonpandasmatplotlibseaborn
Tronald Dump
Tronald Dump

Reputation: 1350

Seaborn Bar Plot Ordering

I have a pandas dataframe that has two columns.

I need the plot ordered by the "Count" Column.

dicti=({'37':99943,'25':47228,'36':16933,'40':14996,'35':11791,'34':8030,'24' : 6319 ,'2'  :5055 ,'39' :4758 ,'38' :4611  })
pd_df = pd.DataFrame(list(dicti.iteritems()))
pd_df.columns =["Dim","Count"]
plt.figure(figsize=(12,8))
ax = sns.barplot(x="Dim", y= "Count",data=pd_df )
ax.get_yaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: "
{:,}".format(int(x))))
ax.set(xlabel="Dim", ylabel='Count')
for item in ax.get_xticklabels():
    item.set_rotation(90)
for i, v in enumerate(pd_df["Count"].iteritems()):        
    ax.text(i ,v[1], "{:,}".format(v[1]), color='m', va ='bottom', 
    rotation=45)
plt.tight_layout()

Right now the plot is getting ordered by the "Dim" column, I need it ordered by the "Count" column,How can I do this?enter image description here

Upvotes: 45

Views: 177129

Answers (6)

Foreever
Foreever

Reputation: 7488

you can use the order parameter for this.

sns.barplot(x='Id', y="Speed", data=df, order=result['Id'])

Credits to Wayne.

See the rest of his code.

This link is still working for me. But, for the sake of convenience, I'm pasting the author's code here.

result = df.groupby(["Id"])['Speed'].aggregate(np.median).reset_index().sort_values('Speed')
sns.barplot(x='Id', y="Speed", data=df, order=result['Id'])
plt.show()

df

   Id  Speed
0   1     30
1   1     35
2   1     31
3   2     20
4   2     25

result

   Id   Speed
1   2   22.5
0   1   31.0
2   3   80.0

Upvotes: 59

Alva
Alva

Reputation: 168

Try using this. There is no need to sort the dataframe or create extra lists.

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

dicti=({'34':8030,'37':99943,'38':4611,'25':47228,'39':4758,'36':16933,'2':5055,'40':14996,'24':6319,'35':11791})
pd_df = pd.DataFrame(list(dicti.items()))
pd_df.columns =["Dim","Count"]
plt.figure(figsize=(12,8))
ax = sns.barplot(x="Dim", y= "Count",data=pd_df, order=pd_df.sort_values(by=['Count'], ascending=False).set_index('Dim').index)
ax.get_yaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: "{:,}".format(int(x))))
ax.set(xlabel="Dim", ylabel='Count')
for item in ax.get_xticklabels():
    item.set_rotation(90)
#for i, v in enumerate(pd_df["Count"].iteritems()):        
#    ax.text(i ,v[1], "{:,}".format(v[1]), color='m', va ='bottom', 
#    rotation=45)
plt.tight_layout()

output

Note: You may notice that there are 3 lines of code that were turned to comments. This is because @Tronald Dump asked about the Seaborn Bar Plot functionality specifically, but there was code to display custom magenta labels that doesn't account for the usage of the optional "order" parameter of the seaborn.barplot function. Therefore, this serves as a valid answer for the OP, but specially for future visitors.

Upvotes: 2

Vojtech Stas
Vojtech Stas

Reputation: 749

To make a specific order, I recommend to create a list and then order by it:

order_list = ['first', 'second', 'third']
sns.barplot(x=df['x'], y=df['y'], order=order_list)

Upvotes: 9

Federico
Federico

Reputation: 781

You can use the following code

import seaborn as sns

iris = sns.load_dataset("iris")
order = iris.groupby(["species"])["sepal_width"].mean().sort_values().index

sns.barplot(x="species", y="sepal_width", data=iris, order=order)

enter image description here

You specify ascending=False if you want to sort them from biggest to smallest.

order = iris.groupby(["species"])["sepal_width"].mean().sort_values(ascending=False).index

Upvotes: 1

Veera Srikanth
Veera Srikanth

Reputation: 496

Prepare the data frame such that it is ordered by the column that you want.

Now pass that as a parameter to function.

import pandas as pd
import seaborn as sns

dicti=({'37': 99943,'25': 47228,'36': 16933,'40': 14996,'35': 11791,'34': 8030,'24': 6319 ,'2': 5055 ,'39': 4758 ,'38' :4611})
pd_df = pd.DataFrame(list(dicti.items()))
pd_df.columns =["Dim", "Count"]

# Here the dataframe is already sorted if not use the below line
# pd_df = pd_df.sort_values('Count').reset_index()
# or 
# pd_df = pd_df.sort_values('Count',ascending=False).reset_index()

sns.barplot(x='Dim', y='Count', data=pd_df, order=pd_df['Dim'])`

enter image description here

Upvotes: 13

Serenity
Serenity

Reputation: 36705

You have to sort your dataframe in desired way and the reindex it to make new ascending / descending index. After that you may plot bar graph with index as x values. Then set set labels by Dim column of your dataframe:

import matplotlib.pylab as plt
import pandas as pd
import seaborn as sns

dicti=({'37':99943,'25':47228,'36':16933,'40':14996,'35':11791,'34':8030,'24' : 6319 ,'2'  :5055 ,'39' :4758 ,'38' :4611  })
pd_df = pd.DataFrame(list(dicti.items()))
pd_df.columns =["Dim","Count"]
print (pd_df)
# sort df by Count column
pd_df = pd_df.sort_values(['Count']).reset_index(drop=True)
print (pd_df)

plt.figure(figsize=(12,8))
# plot barh chart with index as x values
ax = sns.barplot(pd_df.index, pd_df.Count)
ax.get_yaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: "{:,}".format(int(x))))
ax.set(xlabel="Dim", ylabel='Count')
# add proper Dim values as x labels
ax.set_xticklabels(pd_df.Dim)
for item in ax.get_xticklabels(): item.set_rotation(90)
for i, v in enumerate(pd_df["Count"].iteritems()):        
    ax.text(i ,v[1], "{:,}".format(v[1]), color='m', va ='bottom', rotation=45)
plt.tight_layout()
plt.show()

enter image description here

Upvotes: 21

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