Hi I am working on the following dataset
Dataset
df = pd.read_csv('https://github.com/datameet/india-election-data/blob/master/parliament-elections/parliament.csv')
df.groupby(['YEAR','PARTY'])['PC'].nunique()
How do I create a stacked bar plot with year as x axis and pc count as y axis and stacked column labels as party names. Basically I want to display the top 5 parties each year by value, bucket all other parties (excluding IND) as 'others'
Want to visualize something like this Election Viz
IIUC this should work:
sd = df.groupby(['YEAR','PARTY'])['PC'].nunique().reset_index()
sd.pivot(index='YEAR',values='PC',columns='PARTY').plot(kind='bar',stacked=True,figsize=(8,8))
Related
I am working on a dataset which shows the budget spent on movies. I want make a plot which contains the top 15 highest budget movies.
#sort the 'budget' column in decending order and store it in the new dataframe.
info = pd.DataFrame(dp['budget'].sort_values(ascending = False))
info['original_title'] = dp['original_title']
data = list(map(str,(info['original_title'])))
#extract the top 10 budget movies data from the list and dataframe.
x = list(data[:10])
y = list(info['budget'][:10])
This was the ouput i got
C:\Users\Phillip\AppData\Local\Temp\ipykernel_7692\1681814737.py:2: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.
y = list(info['budget'][:5])
I'm new to the data analysis scene so i'm confused on how else to go about the problem
A simple example using a movie dataset I found online:
import pandas as pd
url = "https://raw.githubusercontent.com/erajabi/Python_examples/master/movie_sample_dataset.csv"
df = pd.read_csv(url)
# Bar plot of 15 highest budgets:
df.nlargest(n=15, columns="budget").plot.bar(x="movie_title", y="budget")
You can customize your plot in various ways by adding arguments to the .bar(...) call.
Hy all! i have some Dataframes that look look like this
FSvalue
Season
1966-1967 288.0
1967-1968 129.0
1968-1969 384.0
1969-1970 507.0
1970-1971 236.0
FSvalue
Season
1965-1966 384.000
1966-1967 496.999
1967-1968 197.000
1968-1969 382.000
1969-1970 458.000
Now i want to combine the Frames, Season should stay in first place and FSvalue should be added
from the other frames to one big frame. The problem is, that they dont start with the same season.
I want the values from the second frame to stand at the correct position in the new combined frame.
So the Final Frame should have 1 column with Season and 4 columns with the values(i have 4 frames in total), but at the right Row depending on the season
my code looks like this, but x4 for all files:
df_a = pd.read_csv('xxxFreshSnowperSeason.csv', delimiter=",", dtype = {'Season': str, 'FSvalue': float})
df_a.set_index('Season', inplace=True)
Use concat with aggregate sum:
df = pd.concat([df_a, df_b, df_c, df_d], axis=1).groupby(level=0).sum()
The DataFrame can be viewed here: Global Suicide Dataset
I have made a pivot table with country and year as indices using the following code:
df1 = pd.pivot_table(df, index = ['country', 'year'],
values=['suicides_no','gdp_per_capita ($)', 'population', 'suicides/100k pop'],
aggfunc = {"suicides_no" : np.sum
,"gdp_per_capita ($)" : np.mean
,"population" : np.mean
,"suicides/100k pop" : np.mean})
Output:
Now for my project, i want to visualize how does the suicides_no vary with the gdp_per_capita for a country over the years. But I am unable to plot it. Can somebody please help me out?
First lets convert indexes to columns using df1.reset_index(inplace=True)
Now, you can draw this in a scatter plot where the main features are - Year (preferably on x-axis) and suicides_no (on y-axis). The gdp_per_capita will go as size of the dots.
In this case you have two options:
Draw different plots for each country. (gdp will be shown as hue)
sns.catplot(x='year', y='suicides_no', row='country', hue='gdp_per_capita ($)', data=df1)
Draw everything in a single plot. Scatter plot with GDP as dot size, and Country as Color (hue)
sns.scatterplot(x='year', y='suicides_no', hue='country', size='gdp_per_capita ($)', data=df1)
I am reading huge csv file using pandas module.
filename = pd.read_csv(filepath)
Converted to Dataframe,
df = pd.DataFrame(filename, index=None)
From the csv file, I am concerned with the three columns of name country, year, and value.
I have groupby the country names and sum the values of it as in the following code and plot it as a bar graph.
df.groupby('country').value.sum().plot(kind='bar')
where, x axis is country and y axis is value.
Now, I want to make this bar graph as a stacked bar and used the third column year with different color bars representing each year. Looking forward for an easy way.
Note that, year column contains years from 2000 to 2019.
Thanks.
from what i understand you should try something like :
df.groupby(['country', 'Year']).value.sum().unstack().plot(kind='bar', stacked=True)
I have monthly data of 6 variables from 2014 until 2018 in one dataset.
I'm trying to draw 6 subplots (one for each variable) with monthly X axis (Jan, Feb....) and 5 series (one for each year) with their legend.
This is part of the data:
I created 5 series (one for each year) per variable (30 in total) and I'm getting the expected output but using MANY lines of code.
What is the best way to achieve this using less lines of code?
This is an example how I created the series:
CL2014 = data_total['Charity Lottery'].where(data_total['Date'].dt.year == 2014)[0:12]
CL2015 = data_total['Charity Lottery'].where(data_total['Date'].dt.year == 2015)[12:24]
This is an example of how I'm plotting the series:
axCL.plot(xvals, CL2014)
axCL.plot(xvals, CL2015)
axCL.plot(xvals, CL2016)
axCL.plot(xvals, CL2017)
axCL.plot(xvals, CL2018)
There's no need to litter your namespace with 30 variables. Seaborn makes the job very easy but you need to normalize your dataframe first. This is what "normalized" or "unpivoted" looks like (Seaborn calls this "long form"):
Date variable value
2014-01-01 Charity Lottery ...
2014-01-01 Racecourse ...
2014-04-01 Bingo Halls ...
2014-04-01 Casino ...
Your screenshot is a "pivoted" or "wide form" dataframe.
df_plot = pd.melt(df, id_vars='Date')
df_plot['Year'] = df_plot['Date'].dt.year
df_plot['Month'] = df_plot['Date'].dt.strftime('%b')
import seaborn as sns
plot = sns.catplot(data=df_plot, x='Month', y='value',
row='Year', col='variable', kind='bar',
sharex=False)
plot.savefig('figure.png', dpi=300)
Result (all numbers are randomly generated):
I would try using .groupby(), it is really powerful for parsing down things like this:
for _, group in data_total.groupby([year, month])[[x_variable, y_variable]]:
plt.plot(group[x_variables], group[y_variables])
So here the groupby will separate your data_total DataFrame into year/month subsets, with the [[]] on the end to parse it down to the x_variable (assuming it is in your data_total DataFrame) and your y_variable, which you can make any of those features you are interested in.
I would decompose your datetime column into separate year and month columns, then use those new columns inside that groupby as the [year, month]. You might be able to pass in the dt.year and dt.month like you had before... not sure, try it both ways!