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)
Related
I have a multi index dataframe, with the two indices being Sample and Lithology
Sample 20EC-P 20EC-8 20EC-10-1 ... 20EC-43 20EC-45 20EC-54
Lithology Pd Di-Grd Gb ... Hbl Plag Pd Di-Grd Gb
Rb 7.401575 39.055118 6.456693 ... 0.629921 56.535433 11.653543
Ba 24.610102 43.067678 10.716841 ... 1.073115 58.520532 56.946630
Th 3.176471 19.647059 3.647059 ... 0.823529 29.647059 5.294118
I am trying to put it into a seaborn lineplot as such.
spider = sns.lineplot(data = data, hue = data.columns.get_level_values("Lithology"),
style = data.columns.get_level_values("Sample"),
dashes = False, palette = "deep")
The lineplot comes out as
1
I have two issues. First, I want to format hues by lithology and style by sample. Outside of the lineplot function, I can successfully access sample and lithology using data.columns.get_level_values, but in the lineplot they don't seem to do anything and I haven't figured out another way to access these values. Also, the lineplot reorganizes the x-axis by alphabetical order. I want to force it to keep the same order as the dataframe, but I don't see any way to do this in the documentation.
To use hue= and style=, seaborn prefers it's dataframes in long form. pd.melt() will combine all columns and create new columns with the old column names, and a column for the values. The index too needs to be converted to a regular column (with .reset_index()).
Most seaborn functions use order= to set an order on the x-values, but with lineplot the only way is to make the column categorical applying a fixed order.
from matplotlib import pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
column_tuples = [('20EC-P', 'Pd '), ('20EC-8', 'Di-Grd'), ('20EC-10-1 ', 'Gb'),
('20EC-43', 'Hbl Plag Pd'), ('20EC-45', 'Di-Grd'), ('20EC-54', 'Gb')]
col_index = pd.MultiIndex.from_tuples(column_tuples, names=["Sample", "Lithology"])
data = pd.DataFrame(np.random.uniform(0, 50, size=(3, len(col_index))), columns=col_index, index=['Rb', 'Ba', 'Th'])
data_long = data.melt(ignore_index=False).reset_index()
data_long['index'] = pd.Categorical(data_long['index'], data.index) # make categorical, use order of the original dataframe
ax = sns.lineplot(data=data_long, x='index', y='value',
hue="Lithology", style="Sample", dashes=False, markers=True, palette="deep")
ax.set_xlabel('')
ax.legend(loc='upper left', bbox_to_anchor=(1.01, 1.02))
plt.tight_layout() # fit legend and labels into the figure
plt.show()
The long dataframe looks like:
index Sample Lithology value
0 Rb 20EC-P Pd 6.135005
1 Ba 20EC-P Pd 6.924961
2 Th 20EC-P Pd 44.270570
...
I have a pandas dataframe df for which I plot a multi-histogram as follow :
df.hist(bins=20)
This give me a result that look like this (Yes this exemple is ugly since there is only one data per histogram, sorry) :
I have a subplot for each numerical column of my dataframe.
Now I want all my histograms to have an X-axis between 0 and 1. I saw that the hist() function take a ax parameter, but I cannot manage to make it work.
How is it possible to do that ?
EDIT :
Here is a minmal example :
import pandas as pd
import matplotlib.pyplot as plt
myArray = [(0,0,0,0,0.5,0,0,0,1),(0,0,0,0,0.5,0,0,0,1)]
myColumns = ['col1','col2','col3','co4','col5','col6','col7','col8','col9']
df = pd.DataFrame(myArray,columns=myColumns)
print(df)
df.hist(bins=20)
plt.show()
Here is a solution that works, but for sure is not ideal:
import pandas as pd
import matplotlib.pyplot as plt
myArray = [(0,0,0,0,0.5,0,0,0,1),(0,0,0,0,0.5,0,0,0,1)]
myColumns = ['col1','col2','col3','co4','col5','col6','col7','col8','col9']
df = pd.DataFrame(myArray,columns=myColumns)
print(df)
ax = df.hist(bins=20)
for x in ax:
for y in x:
y.set_xlim(0,1)
plt.show()
I have two dataFrames that I would like to plot into a single graph. Here's a basic code:
#!/usr/bin/python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
scenarios = ['scen-1', 'scen-2']
for index, item in enumerate(scenarios):
df = pd.DataFrame({'A' : np.random.randn(4)})
print df
df.plot()
plt.ylabel('y-label')
plt.xlabel('x-label')
plt.title('Title')
plt.show()
However, this only plots the last dataFrame. If I use pd.concat() it plots one line with the combined values.
How can I plot two lines, one for the first dataFrame and one for the second one?
You need to put your plot in the for loop.
If you want them on a single plot then you need to use plot's ax kwarg to put them to plot on the same axis. Here I have created a fresh axis using subplots but this could be an already populated axis,
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
scenarios = ['scen-1', 'scen-2']
fig, ax = plt.subplots()
for index, item in enumerate(scenarios):
df = pd.DataFrame({'A' : np.random.randn(4)})
print df
df.plot(ax=ax)
plt.ylabel('y-label')
plt.xlabel('x-label')
plt.title('Title')
plt.show()
The plot function is only called once, and as you say this is with the last value of df. Put df.plot() inside the loop.
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.
tmpdf.boxplot(['original','new'], by = 'by column', ax = ax, sym = '')
gets me a plot like this
I want to compare "original" with "new", how can I arrange to put the two "0" boxes in one panel and the two "1" boxes in another panel? And of course swap the labelling with that.
Thanks
Here is a sample dataset to demonstrate.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# simulate some artificial data
# ==========================================
np.random.seed(0)
df = pd.DataFrame(np.random.rand(10,2), columns=['original', 'new'] )
df['by column'] = pd.Series([0,0,0,0,1,1,1,1,1,1])
# your original plot
ax = df.boxplot(['original', 'new'], by='by column', figsize=(12,6))
To get desired output, use groupby explicitly out of boxplot, so that we iterate over all subgroups, and plot a boxplot for each.
ax = df[['original', 'new']].groupby(df['by column']).boxplot(figsize=(12,6))