I have this CSV data file, I'm trying to make a pie chart using this data
I'm a beginner in python and don't understand how to create a pie chart using the three columns, please help!
working solution code would be more helpful!
My code:
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv ('chart_work.csv')
product_data = df["Product Name;"]
bug_data = df["Number Of Bugs"]
colors = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#8c564b"]
plt.pie(bug_data , labels=product_data , colors=colors,
autopct='%1.1f%%', shadow=True, startangle=140)
plt.show()
the pie chart which is outputed by this code is distorted, any help?
Chart I'm getting:
This is very simple.
import pandas as pd
from matplotlib.pyplot import pie, axis, show
%matplotlib inline
df = pd.read_csv ('chart_work.csv')
sums = df.groupby(df["Product Name;"])["Number Of Bugs"].sum()
axis('equal');
pie(sums, labels=sums.index);
show()
The pie chart does not 'know' that you want all items with same product name grouped and summed over in your chart. so you have to do that first:
df = df.groupby(["Product Name;"]).sum()
This sets the product name column as index of the df so change your product_data column selection to this:
product_data = df.index
import matplotlib.pyplot as plt
sizes=[89,80,90,100,75]
lables=["swetha","yokesh","iswarya","ranjeeth","deepika"]
plt.pie(sizes,lables=lables,autopct="%.2f")
plt.axes().set_aspect("equal")
plt.show()
Related
Could you please help me if you know how to make a pie chart in Python from it?
This is a reproducible example how the df looks like. However, I have way more rows over there.
import pandas as pd
data = [["70%"], ["20%"], ["10%"]]
example = pd.DataFrame(data, columns = ['percentage'])
example.index = ['Lasiogl', 'Centella', 'Osmia']
example
You can use matplotlib to plot the pie chart using dataframe and its indexes as labels of the chart:
import matplotlib.pyplot as plt
import pandas as pd
data = ['percentage':["70%"], ["20%"], ["10%"]]
example = pd.DataFrame(data, columns = ['percentage'])
my_labels = 'Lasiogl', 'Centella', 'Osmia'
plt.pie(example,labels=my_labels,autopct='%1.1f%%')
plt.show()
I have output nested dictionary variable called all_count_details_dictionary. Using that variable I saved data to the CSV file using the following command.
import pandas as pd
csv_path = '../results_v6/output_01.csv'
# creating pandas dataframe using concat mehtod to extract data from dictionary
df = pd.concat([pd.DataFrame(l) for l in all_count_details_dictionary],axis=1).T
# saving the dataframe to the csv file
df.to_csv(csv_path, index=True)
The output CSV file is just like as below
The CSV file can be download using this link
So I used the following code to plot a graph
import matplotlib.pyplot as plt
def extract_csv_gen_plot(csv_path):
length = 1503 #len(dataframe_colums_list)
data = np.genfromtxt(csv_path, delimiter=",", skip_header=True, usecols=range(3, (length+1)))
print(data)
# renaming data axes
#fig, ax = plt.subplots()
#fig.canvas.draw()
#labels =[item.get_text() for item in ax.get_xticklabels()]
#labels[1] = 'testing'
#ax.set_xticklabels(labels)
#ax.set_xticklabels(list)
#ax.set_yticklabels(list)
#plt.setp(ax.get_xticklabels(), rotation = 90)
plt.imshow(data, cmap='hot',interpolation='nearest')
plt.show()
I tried to get the column labels and case details labels into the graph axes, but it doesn't work out. Can anyone please tell me there is any other best method to plot this table into a heat map than this?
Thank you!
I would suggest using Pandas, the labels are picked up automatically:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
def extract_csv_gen_plot(csv_path):
data = pd.read_csv(csv_path, index_col=1)
data = data.drop(data.columns[[0, 1]], axis=1)
data.index.names = ['Name']
g = sns.heatmap(data)
g.set_yticklabels(g.get_yticklabels(), rotation=0)
g.set_title('Heatmap')
plt.tight_layout()
plt.show()
extract_csv_gen_plot("output_01.csv")
I recommend using Seaborn, they have a heatmap plotting function that works very well with Pandas DataFrames
import seaborn as sns
sns.heatmap(data)
https://seaborn.pydata.org/generated/seaborn.heatmap.html
I saw a sample from the internet to build a simple pie chart from Matplotlib but not sure how to embed it with my dataset (https://gist.github.com/datomnurdin/33961755b306bc67e4121052ae87cfbc).
from pandas import DataFrame
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('data.csv')
my_labels = 'Positive','Neutral','Negative'
my_colors = ['lightblue','lightsteelblue','silver']
plt.pie(df, labels=my_labels, autopct='%1.1f%%', startangle=15, shadow = True, colors=my_colors)
plt.title('Sentiment Overview')
plt.axis('equal')
plt.show()
P.S: The dataset didn't contain any labels, only values.
I would do something like this:
my_labels = {1:'Positive',0:'Neutral',-1:'Negative'}
my_colors = ['lightblue','lightsteelblue','silver']
# count the values to plot pie chart
s = df.sentiment.map(my_labels).value_counts()
plt.pie(s, labels=s.index, autopct='%1.1f%%', colors=my_colors)
# also
# s.plot.pie(autopct='%1.1f%%', colors=my_colors)
plt.show()
Output:
I have a dataframe with 3 variables:
data= [["2019/oct",10,"Approved"],["2019/oct",20,"Approved"],["2019/oct",30,"Approved"],["2019/oct",40,"Approved"],["2019/nov",20,"Under evaluation"],["2019/dec",30,"Aproved"]]
df = pd.DataFrame(data, columns=['Period', 'Observations', 'Result'])
I want a barplot grouped by the Period column, showing all the values ​​contained in the Observations column and colored with the Result column.
How can I do this?
I tried the sns.barplot, but it joined the values in Observations column in just one bar(mean of the values).
sns.barplot(x='Period',y='Observations',hue='Result',data=df,ci=None)
Plot output
Assuming that you want one bar for each row, you can do as follows:
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
result_cat = df["Result"].astype("category")
result_codes = result_cat.cat.codes.values
cmap = plt.cm.Dark2(range(df["Result"].unique().shape[0]))
patches = []
for code in result_cat.cat.codes.unique():
cat = result_cat.cat.categories[code]
patches.append(mpatches.Patch(color=cmap[code], label=cat))
df.plot.bar(x='Period',
y='Observations',
color=cmap[result_codes],
legend=False)
plt.ylabel("Observations")
plt.legend(handles=patches)
If you would like it grouped by the months, and then stacked, please use the following (note I updated your code to make sure one month had more than one status), but not sure I completely understood your question correctly:
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
data= [["2019/oct",10,"Approved"],["2019/oct",20,"Approved"],["2019/oct",30,"Approved"],["2019/oct",40,"Under evaluation"],["2019/nov",20,"Under evaluation"],["2019/dec",30,"Aproved"]]
df = pd.DataFrame(data, columns=['Period', 'Observations', 'Result'])
df.groupby(['Period', 'Result'])['Observations'].sum().unstack('Result').plot(kind='bar', stacked=True)
How can I achieve that using matplotlib?
Here is my code with the data you provided. As there's no class [they are all different, despite your first example in your question does have classes], I gave colors based on the numbers. You can definitely start alone from here, whatever result you want to achieve. You just need pandas, seaborn and matplotlib:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# import xls
df=pd.read_excel('data.xlsx')
# exclude Ranking values
df1 = df.ix[:,1:-1]
# for each element it takes the value of the xls cell
df2=df1.applymap(lambda x: float(x.split('\n')[1]))
# now plot it
df_heatmap = df2
fig, ax = plt.subplots(figsize=(15,15))
sns.heatmap(df_heatmap, square=True, ax=ax, annot=True, fmt="1.3f")
plt.yticks(rotation=0,fontsize=16);
plt.xticks(fontsize=12);
plt.tight_layout()
plt.savefig('dfcolorgraph.png')
Which produces the following picture.