My question is similar to Making multiple pie charts out of a pandas dataframe (one for each row).
However, instead of each row, I am looking for each column in my case.
I can make pie chart for each column, however, as I have 12 columns the pie charts are too much close to each other.
I have used this code:
fig, axes = plt.subplots(4, 3, figsize=(10, 6))
for i, (idx, row) in enumerate(df.iterrows()):
ax = axes[i // 3, i % 3]
row = row[row.gt(row.sum() * .01)]
ax.pie(row, labels=row.index, startangle=30)
ax.set_title(idx)
fig.subplots_adjust(wspace=.2)
and I have the following result
But I want is on the other side. I need to have 12 pie charts (becuase I have 12 columns) and each pie chart should have 4 sections (which are leg, car, walk, and bike)
and if I write this code
fig, axes = plt.subplots(4,3)
for i, col in enumerate(df.columns):
ax = axes[i // 3, i % 3]
plt.plot(df[col])
then I have the following results:
and if I use :
plot = df.plot.pie(subplots=True, figsize=(17, 8),labels=['pt','car','walk','bike'])
then I have the following results:
Which is quite what I am looking for. but it is not possible to read the pie charts. if it can produce in more clear output, then it is better.
As in your linked post I would use matplotlib.pyplot for this. The accepted answer uses plt.subplots(2, 3) and I would suggest doing the same for creating two rows with each 3 plots in them.
Like this:
fig, axes = plt.subplots(2,3)
for i, col in enumerate(df.columns):
ax = axes[i // 3, i % 3]
ax.plot(df[col])
Finally, I understood that if I swap rows and columns
df_sw = df.T
Then I can use the code in the examples:
Making multiple pie charts out of a pandas dataframe (one for each row)
Related
I have a dataframe which contains the 3 columns of data (P, EP and Q) for each of the three catchment areas. I need to make a subplot of each catchment area showing the 3 columns of data that belong to this catchment area using one loop.
I did manage to make the three subplots without using a loop, but don't get how I am supposed to use one loop.
df = pd.read_excel('catchment_water_balance_data_ex2.xlsx', index_col=0, parse_dates=[0], skiprows=4)
df_monthly = df.resample('M').mean()
fig, axs = plt.subplots(3)
catchment_1 = df_monthly[['P1', 'EP1', 'Q1']]
catchment_2 = df_monthly[['P2', 'EP2', 'Q2']]
catchment_3 = df_monthly[['P3', 'EP3', 'Q3']]
axs[0].plot(catchment_1)
axs[1].plot(catchment_2)
axs[2].plot(catchment_3)
fig.suptitle('Water data of 3 catchments')
fig.supylabel('mm/day');
enter image description here
I'm trying to draw bar-charts with counts of unique values for all columns in a Pandas DataFrame. Kind of what df.hist() does for numerical columns, but I have categorical columns.
I'd prefer to use the object-oriented approach, because if feels more natural and explicit to me.
I'd like to have multiple Axes (subplots) within a single Figure, in a grid fashion (again like what df.hist() does).
My solution below does exactly what I want, but it feels cumbersome. I doubt whether I really need the direct dependency on Matplotlib (and all the code for creating the Figure, removing the unused Axes etc.). I see that pandas.Series.plot has parameters subplots and layout which seem to point to what I want, but maybe I'm totally off here. I tried looping over the columns in my DataFrame and apply these parameters, but I cannot figure it out.
Does anyone know a more compact way to do what I'm trying to achieve?
# Defining the grid-dimensions of the Axes in the Matplotlib Figure
nr_of_plots = len(ames_train_categorical.columns)
nr_of_plots_per_row = 4
nr_of_rows = math.ceil(nr_of_plots / nr_of_plots_per_row)
# Defining the Matplotlib Figure and Axes
figure, axes = plt.subplots(nrows=nr_of_rows, ncols=nr_of_plots_per_row, figsize=(25, 50))
figure.subplots_adjust(hspace=0.5)
# Plotting on the Axes
i, j = 0, 0
for column_name in ames_train_categorical:
if ames_train_categorical[column_name].nunique() <= 30:
axes[i][j].set_title(column_name)
ames_train_categorical[column_name].value_counts().plot(kind='bar', ax=axes[i][j])
j += 1
if j % nr_of_plots_per_row == 0:
i += 1
j = 0
# Cleaning up unused Axes
# plt.subplots creates a square grid of Axes. On the last row, not all Axes will always be used. Unused Axes are removed here.
axes_flattened = axes.flatten()
for ax in axes_flattened:
if not ax.has_data():
ax.remove()
Edit: alternative idea
Using the pyplot/state-machine WoW, you could do it like this with very limited lines of code. But this also has the downside that every graph gets it's own figure, you they're not nicely arranged in a grid.
for column_name in ames_train_categorical:
ames_train_categorical[column_name].value_counts().plot(kind='bar')
plt.show()
Desired output
With the following toy dataframe:
import pandas as pd
df = pd.DataFrame(
{
"MS Zoning": ["RL", "FV", "RL", "RH", "RL", "RL"],
"Street": ["Pave", "Pave", "Pave", "Grvl", "Pave", "Pave"],
"Alley": ["Grvl", "Grvl", "Grvl", "Grvl", "Pave", "Pave"],
"Utilities": ["AllPub", "NoSewr", "AllPub", "AllPub", "NoSewr", "AllPub"],
"Land Slope": ["Gtl", "Mod", "Sev", "Mod", "Sev", "Sev"],
}
)
Here is a bit more idiomatic way to do it:
import math
from matplotlib import pyplot as plt
size = math.ceil(df.shape[1]** (1/2))
fig = plt.figure()
for i, col in enumerate(df.columns):
fig.add_subplot(size, size, i + 1)
df[col].value_counts().plot(kind="bar", ax=plt.gca(), title=col, rot=0)
fig.tight_layout()
I have grouped data in 2 separate dataframes and want to plot them together with 2 separate lines in one plot.
I have grouped the data as I needed and plotted separate graphs based on the grouped data.
grouped_men = df_men.groupby('age').mean()[['oldpeak']]
grouped_women = df_women.groupby('age').mean()[['oldpeak']]
grouped_men.plot(kind='line',title='Mens age vs oldpeak')
grouped_women.plot(kind='line',title='Womens age vs oldpeak')
But now instead of 2 separate plots, i need to plot one single graph with 2 lines of both men and women.
Current plot look like this:
You need to specify axes where Pandas should put the plots. Try the following:
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
grouped_men.plot(kind='line', ax=ax, label='Mens age vs oldpeak')
grouped_women.plot(kind='line', ax=ax, label='Womens age vs oldpeak')
plt.gca().legend(title="Legend title") # Changes
plt.show()
I'm having trouble understanding Pandas subplots - and how to create axes so that all subplots are shown (not over-written by subsequent plot).
For each "Site", I want to make a time-series plot of all columns in the dataframe.
The "Sites" here are 'shark' and 'unicorn', both with 2 variables. The output should be be 4 plotted lines - the time-indexed plot for Var 1 and Var2 at each site.
Make Time-Indexed Data with Nans:
df = pd.DataFrame({
# some ways to create random data
'Var1':pd.np.random.randn(100),
'Var2':pd.np.random.randn(100),
'Site':pd.np.random.choice( ['unicorn','shark'], 100),
# a date range and set of random dates
'Date':pd.date_range('1/1/2011', periods=100, freq='D'),
# 'f':pd.np.random.choice( pd.date_range('1/1/2011', periods=365,
# freq='D'), 100, replace=False)
})
df.set_index('Date', inplace=True)
df['Var2']=df.Var2.cumsum()
df.loc['2011-01-31' :'2011-04-01', 'Var1']=pd.np.nan
Make a figure with a sub-plot for each site:
fig, ax = plt.subplots(len(df.Site.unique()), 1)
counter=0
for site in df.Site.unique():
print(site)
sitedat=df[df.Site==site]
sitedat.plot(subplots=True, ax=ax[counter], sharex=True)
ax[0].title=site #Set title of the plot to the name of the site
counter=counter+1
plt.show()
However, this is not working as written. The second sub-plot ends up overwriting the first. In my actual use case, I have 14 variable number of sites in each dataframe, as well as a variable number of 'Var1, 2, ...'. Thus, I need a solution that does not require creating each axis (ax0, ax1, ...) by hand.
As a bonus, I would love a title of each 'site' above that set of plots.
The current code over-writes the first 'Site' plot with the second. What I missing with the axes here?!
When you are using DataFrame.plot(..., subplot=True) you need to provide the correct number of axes that will be used for each column (and with the right geometry, if using layout=). In your example, you have 2 columns, so plot() needs two axes, but you are only passing one in ax=, therefore pandas has no choice but to delete all the axes and create the appropriate number of axes itself.
Therefore, you need to pass an array of axes of length corresponding to the number of columns you have in your dataframe.
# the grouper function is from itertools' cookbook
from itertools import zip_longest
def grouper(iterable, n, fillvalue=None):
"Collect data into fixed-length chunks or blocks"
# grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx"
args = [iter(iterable)] * n
return zip_longest(*args, fillvalue=fillvalue)
fig, axs = plt.subplots(len(df.Site.unique())*(len(df.columns)-1),1, sharex=True)
for (site,sitedat),axList in zip(df.groupby('Site'),grouper(axs,len(df.columns)-1)):
sitedat.plot(subplots=True, ax=axList)
axList[0].set_title(site)
plt.tight_layout()
I am using Seaborn to make boxplots from pandas dataframes. Seaborn boxplots seem to essentially read the dataframes the same way as the pandas boxplot functionality (so I hope the solution is the same for both -- but I can just use the dataframe.boxplot function as well). My dataframe has 12 columns and the following code generates a single plot with one boxplot for each column (just like the dataframe.boxplot() function would).
fig, ax = plt.subplots()
sns.set_style("darkgrid", {"axes.facecolor":"darkgrey"})
pal = sns.color_palette("husl",12)
sns.boxplot(dataframe, color = pal)
Can anyone suggest a simple way of overlaying all the values (by columns) while making a boxplot from dataframes?
I will appreciate any help with this.
This hasn't been added to the seaborn.boxplot function yet, but there's something similar in the seaborn.violinplot function, which has other advantages:
x = np.random.randn(30, 6)
sns.violinplot(x, inner="points")
sns.despine(trim=True)
A general solution for the boxplot for the entire dataframe, which should work for both seaborn and pandas as their are all matplotlib based under the hood, I will use pandas plot as the example, assuming import matplotlib.pyplot as plt already in place. As you have already have the ax, it would make better sense to just use ax.text(...) instead of plt.text(...).
In [35]:
print df
V1 V2 V3 V4 V5
0 0.895739 0.850580 0.307908 0.917853 0.047017
1 0.931968 0.284934 0.335696 0.153758 0.898149
2 0.405657 0.472525 0.958116 0.859716 0.067340
3 0.843003 0.224331 0.301219 0.000170 0.229840
4 0.634489 0.905062 0.857495 0.246697 0.983037
5 0.573692 0.951600 0.023633 0.292816 0.243963
[6 rows x 5 columns]
In [34]:
df.boxplot()
for x, y, s in zip(np.repeat(np.arange(df.shape[1])+1, df.shape[0]),
df.values.ravel(), df.values.astype('|S5').ravel()):
plt.text(x,y,s,ha='center',va='center')
For a single series in the dataframe, a few small changes is necessary:
In [35]:
sub_df=df.V1
pd.DataFrame(sub_df).boxplot()
for x, y, s in zip(np.repeat(1, df.shape[0]),
sub_df.ravel(), sub_df.values.astype('|S5').ravel()):
plt.text(x,y,s,ha='center',va='center')
Making scatter plots is also similar:
#for the whole thing
df.boxplot()
plt.scatter(np.repeat(np.arange(df.shape[1])+1, df.shape[0]), df.values.ravel(), marker='+', alpha=0.5)
#for just one column
sub_df=df.V1
pd.DataFrame(sub_df).boxplot()
plt.scatter(np.repeat(1, df.shape[0]), sub_df.ravel(), marker='+', alpha=0.5)
To overlay stuff on boxplot, we need to first guess where each boxes are plotted at among xaxis. They appears to be at 1,2,3,4,..... Therefore, for the values in the first column, we want them to be plot at x=1; the 2nd column at x=2 and so on.
Any efficient way of doing it is to use np.repeat, repeat 1,2,3,4..., each for n times, where n is the number of observations. Then we can make a plot, using those numbers as x coordinates. Since it is one-dimensional, for the y coordinates, we will need a flatten view of the data, provided by df.ravel()
For overlaying the text strings, we need a anther step (a loop). As we can only plot one x value, one y value and one text string at a time.
I have the following trick:
data = np.random.randn(6,5)
df = pd.DataFrame(data,columns = list('ABCDE'))
Now assign a dummy column to df:
df['Group'] = 'A'
print df
A B C D E Group
0 0.590600 0.226287 1.552091 -1.722084 0.459262 A
1 0.369391 -0.037151 0.136172 -0.772484 1.143328 A
2 1.147314 -0.883715 -0.444182 -1.294227 1.503786 A
3 -0.721351 0.358747 0.323395 0.165267 -1.412939 A
4 -1.757362 -0.271141 0.881554 1.229962 2.526487 A
5 -0.006882 1.503691 0.587047 0.142334 0.516781 A
Use the df.groupby.boxplot(), you get it done.
df.groupby('Group').boxplot()