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I have created a single stacked bar chart but I want to have them clustered. Exactly something like the picture.
Wondering if it's possible.
link to picture
df = pd.DataFrame(dict(Subsidy=[3, 3, 3],
Bonus=[1, 1, 1],
Expense=[2, 2, 2]),
list('ABC'))
df
ax = df[['Subsidy', 'Bonus']].plot.bar(stacked=True, position=1,
width=.2, ylim=[0, 8], color=['orange', 'red'])
df[['Expense']].plot.bar(ax=ax, position=0, width=.2, color=['green'])
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Currently I have the following excel table that i am importing in pandas. I am trying to transpose the data so that the Name 1, Name 2, etc., is the index column and the dates are now the headers. After, I want to create a new table with this layout that selects the most recent data point based on the dates per name.
transposed_df = df.transpose()
print (transposed_df)
transposed_df.set_index('Name', inplace=True)
latest_date_col_index = df.idxmax(axis=1)
latest_data = df.lookup(df.index, latest_date_col_index)
df_latest = pd.DataFrame(latest_data, index=df.index, columns=['Latest Data'])
print(df_latest)
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I have a pandas dataframe in following format
date ticks value
the ticks vary from 1 to 12 for each date. and there are corresponding values in value column
I want to plot a time series line chart where x-axis represents ticks from 1 to 12, the y-axis represents value and there are multiple lines on the chart, each line representing a new date. How can i achieve this using pandas or any other library like matplotlib
Use:
# making sample df
df = pd.DataFrame({'date':['2020']*12+['2019']*12, 'ticks': list(range(1, 13))*2, 'value': np.random.randint(1,100,24)})
g = df.groupby('date').agg(list).reset_index()
import matplotlib.pyplot as plt
for i, row in g.iterrows():
plt.plot(row['ticks'], row['value'], label = row['date']);
plt.legend();
Output:
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I have a data of dates with times and the industries happened during this dates. For example the data would be something like this:
I want to plot the dates with as months with which industries occurred the most during this months
How can I do that?
So your problem seems to be that you have two different data types which makes creating a graph difficult. However you can reformat the data to the proper types you want which will make creating a graph in the way you intend much easier. Something like this should work for what your wanting.
import pandas as pd
import matplotlib.pyplot as plt
data = pd.DataFrame(
[{'date_raised':pd.to_datetime('2016-01-01 00:00:00'),'primary_industry':'Real Estate'},
{'date_raised':pd.to_datetime('2016-01-10 04:00:00'),'primary_industry':'IT Solutions'},
{'date_raised':pd.to_datetime('2016-01-04 04:00:00'),'primary_industry':'Multimedia'},
{'date_raised':pd.to_datetime('2016-01-05 04:00:00'),'primary_industry':'Technology'},
{'date_raised':pd.to_datetime('2016-01-09 04:00:00'),'primary_industry':'Technology'}]
)
#Group data for monthly occurrences
result = data.sort_values('date_raised').groupby([data['date_raised'].dt.strftime('%B')])['primary_industry'].value_counts().unstack(level=1)
result.index.name = None #Remove index name "date_raised"
result.columns.names = [None] #Remove series name "primary_industry"
#Plot data
ax = result.plot(kind='bar',use_index=True,rot=1)
ax.set_xlabel('Month')
ax.set_ylabel('Total Occurrences')
plt.show()
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I have a 1000*8 dataset and each column represent the price of a stock in different time so there are 8 stocks. I want to draw 8 boxplots for all the stocks to examine the extreme values in a loop in python. Could you please tell me how I can do that?
As a quick alternative to using matplotlib directly, Pandas has a reasonable boxplot function that could be used.
df = pd.DataFrame(np.random.randn(1000, 8), columns=list('ABCDEFGH'))
df.boxplot(column = list(df.columns))
edit: Just realise your question asked to do this in a loop.
for c in df.columns:
fig, ax = plt.subplots()
ax = df.boxplot(column = c)
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I have a data frame with 1000 rows and 10 columns.
3 of these columns are 'total_2013', 'total_2014' and 'total_2015'
I would like to create a new column, containing the average of total over these 3 years for each row, but ignoring any 0 values.
If you are using pandas:
Use DataFrame.mean leveraging the skipna attribute.
First replace 0 with None using:
columns = ['total_2013', 'total_2014', 'total_2015']
df[columns].replace(0, None)
Then compute the mean:
df["total"] = df[columns].mean(
axis=1, # columns mean
skipna=True # skip nan values
)