I have a dataframe, df, like:
continent | country | counts
------------------------------
East Asia | Hong Kong | 33
East Asia | Japan | 51
Europe | Austria | 10
Europe | Belgium | 3
Europe | Denmark | 15
I want to plot two vertical bar charts, one for each continent, side by side, sharing the same y axis. I've gotten 90% of the way, except for adding the heights of the bars to the subplots. My code so far:
continents_ls = list(set(df["continent"]))
# continents_ls = ["East Asia", "Europe"]
fig, ax = plt.subplots(1, len(continents_ls), figsize=(30, len(continents_ls)*5), sharey=True)
for i in range(len(continents_ls)):
d_temp = df.loc[df["continent"] == continents_ls[i]].groupby("country").size().to_frame().reset_index()
# d_temp is the partition containing info for just one continent
d_temp.columns = ["country", "count"] # name the 'count' column
idx = list(d_temp["country"]) # get the list of countries in that continent
ht_arr = list(d_temp["count"])
ax[i].bar(left=range(len(ht_arr)), height=ht_arr)
ax[i].set_xticks(np.arange(len(idx)))
ax[i].set_xticklabels(idx, size=8, rotation=45)
ax[i].set_title(continents_ls[i], size=23)
ax[i].set_yticklabels(ht_arr, minor=False)
plt.tight_layout()
plt.show()
I've seen examples here and there with labels, but these tend to apply to just one bar chart, not several subplots.
You could do this with only a slight modification to your code. Using this answer: https://stackoverflow.com/a/34598688/42346
for i in range(len(continents_ls)):
d_temp = df.loc[df["continent"] == continents_ls[i]].groupby("country").size().to_frame().reset_index()
# d_temp is the partition containing info for just one continent
d_temp.columns = ["country", "count"] # name the 'count' column
idx = list(d_temp["country"]) # get the list of countries in that continent
ht_arr = list(d_temp["count"])
ax[i].bar(left=range(len(ht_arr)), height=ht_arr)
ax[i].set_xticks(np.arange(len(idx)))
ax[i].set_xticklabels(idx, size=8, rotation=45)
ax[i].set_title(continents_ls[i], size=23)
ax[i].set_yticklabels(ht_arr, minor=False)
if i == 0: # only for the first barplot
for p in ax[i].patches:
ax[i].annotate("%.2f" % p.get_height(), (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')
Related
I have a dataset, df that looks like this:
Date
Code
City
State
Quantity x
Quantity y
Population
Cases
Deaths
2019-01
10001
Los Angeles
CA
445
0
0
2019-01
10002
Sacramento
CA
4450
556
0
0
2020-03
12223
Houston
TX
440
4440
35000000
23
11
...
...
...
...
...
...
...
...
...
2021-07
10002
Sacramento
CA
3220
NA
5444000
211
22
My start and end date are the same for all cities. I have over 4000 different cities, and would like to plot a 2-yaxis graph for each city, using something similar to the following code:
import matplotlib.pyplot as plt
fig, ax1 = plt.subplots(figsize=(9,9))
color = 'tab:red'
ax1.set_xlabel('Date')
ax1.set_ylabel('Quantity X', color=color)
ax1.plot(df['Quantity x'], color=color)
ax1.tick_params(axis='y', labelcolor=color)
ax2 = ax1.twinx()
color2 = 'tab:blue'
ax2.set_ylabel('Deaths', color=color2)
ax2.plot(df['Deaths'], color=color2)
ax2.tick_params(axis='y', labelcolor=color2)
plt.show()
I would like to create a loop so that the code above runs for every Code that is related to a City, with quantity x and deaths, and it saves each graph made into a folder. How can I create a loop that does that, and stops every different Code?
Observations: Some values on df['Quantity x] and df[Population] are left blank.
If I understood you correctly, you are looking for a filtering functionality:
import matplotlib.pyplot as plt
import pandas as pd
def plot_quantity_and_death(df):
# your code
fig, ax1 = plt.subplots(figsize=(9, 9))
color = 'tab:red'
ax1.set_xlabel('Date')
ax1.set_ylabel('Quantity X', color=color)
ax1.plot(df['Quantity x'], color=color)
ax1.tick_params(axis='y', labelcolor=color)
ax2 = ax1.twinx()
color2 = 'tab:blue'
ax2.set_ylabel('Deaths', color=color2)
ax2.plot(df['Deaths'], color=color2)
ax2.tick_params(axis='y', labelcolor=color2)
# save & close addon
plt.savefig(f"Code_{str(df['Code'].iloc[0])}.png")
plt.close()
df = pd.DataFrame() # this needs to be replaced by your dataset
# get unique city codes, loop over them, filter data and plot it
unique_codes = pd.unique(df['Code'])
for code in unique_codes:
filtered_df = df[df['Code'] == code]
plot_quantity_and_death(filtered_df)
I have a table like this:
data = {'Category':["Toys","Toys","Toys","Toys","Food","Food","Food","Food","Food","Food","Food","Food","Furniture","Furniture","Furniture"],
'Product':["AA","BB","CC","DD","SSS","DDD","FFF","RRR","EEE","WWW","LLLLL","PPPPPP","LPO","NHY","MKO"],
'QTY':[100,200,300,50,20,800,300,450,150,320,400,1000,150,900,1150]}
df = pd.DataFrame(data)
df
Out:
Category Product QTY
0 Toys AA 100
1 Toys BB 200
2 Toys CC 300
3 Toys DD 50
4 Food SSS 20
5 Food DDD 800
6 Food FFF 300
7 Food RRR 450
8 Food EEE 150
9 Food WWW 320
10 Food LLLLL 400
11 Food PPPPP 1000
12 Furniture LPO 150
13 Furniture NHY 900
14 Furniture MKO 1150
So, I need to make bars subplots like this (Sum Products in each Category):
My problem is that I can't figure out how to combine categories, series, and aggregation.
I manage to split them into 3 subplots (1 always stays blank) but I can not unite them ...
import matplotlib.pyplot as plt
fig, axarr = plt.subplots(2, 2, figsize=(12, 8))
df['Category'].value_counts().plot.bar(
ax=axarr[0][0], fontsize=12, color='b'
)
axarr[0][0].set_title("Category", fontsize=18)
df['Product'].value_counts().plot.bar(
ax=axarr[1][0], fontsize=12, color='b'
)
axarr[1][0].set_title("Product", fontsize=18)
df['QTY'].value_counts().plot.bar(
ax=axarr[1][1], fontsize=12, color='b'
)
axarr[1][1].set_title("QTY", fontsize=18)
plt.subplots_adjust(hspace=.3)
plt.show()
Out
What do I need to add to combine them?
This would be a lot easier with seaborn and FacetGrid
import pandas as pd
import seaborn as sns
data = {'Category':["Toys","Toys","Toys","Toys","Food","Food","Food","Food","Food","Food","Food","Food","Furniture","Furniture","Furniture"],
'Product':["AA","BB","CC","DD","SSS","DDD","FFF","RRR","EEE","WWW","LLLLL","PPPPPP","LPO","NHY","MKO"],
'QTY':[100,200,300,50,20,800,300,450,150,320,400,1000,150,900,1150]}
df = pd.DataFrame(data)
g = sns.FacetGrid(df, col='Category', sharex=False, sharey=False, col_wrap=2, height=3, aspect=1.5)
g.map_dataframe(sns.barplot, x='Product', y='QTY')
I am coming from R ggplot2 background and, and bit confused in matplotlib plot
here my dataframe
languages = ['en','cs','es', 'pt', 'hi', 'en', 'es', 'es']
counties = ['us','ch','sp', 'br', 'in', 'fr', 'ar', 'pr']
count = [32, 432,43,55,6,23,455,23]
df = pd.DataFrame({'language': languages,'county': counties, 'count' : count})
language county count
0 en us 32
1 cs ch 432
2 es sp 43
3 pt br 55
4 hi in 6
5 en fr 23
6 es ar 455
7 es pr 23
Now I want to plot
A stacked bar chart where x axis show language and y axis show complete count, the big total height show total count for that language and stacked bar show number of countries for that language
A side by side, with same parameters only countries show side by side instead of stacked one
Most of the example show it directly using dataframe and matplotlib plot but I want to plot it in sequential script so I have more control over it, also can edit whatever I want, something like this script
ind = np.arange(df.languages.nunique())
width = 0.35
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
ax.bar(ind, df.languages, width, color='r')
ax.bar(ind, df.count, width,bottom=df.languages, color='b')
ax.set_ylabel('Count')
ax.set_title('Score y language and country')
ax.set_xticks(ind, df.languages)
ax.set_yticks(np.arange(0, 81, 10))
ax.legend(labels=[df.countries])
plt.show()
btw, my panda pivot code for same plotting
df.pivot(index = "Language", columns = "Country", values = "count").plot.bar(figsize=(15,10))
plt.xticks(rotation = 0,fontsize=18)
plt.xlabel('Language' )
plt.ylabel('Count ')
plt.legend(fontsize='large', ncol=2,handleheight=1.5)
plt.show()
import matplotlib.pyplot as plt
languages = ['en','cs','es', 'pt', 'hi', 'en', 'es', 'es']
counties = ['us','ch','sp', 'br', 'in', 'fr', 'ar', 'pr']
count = [32, 432,43,55,6,23,455,23]
df = pd.DataFrame({'language': languages,'county': counties, 'count' : count})
modified = {}
modified['language'] = np.unique(df.language)
country_count = []
total_count = []
for x in modified['language']:
country_count.append(len(df[df['language']==x]))
total_count.append(df[df['language']==x]['count'].sum())
modified['country_count'] = country_count
modified['total_count'] = total_count
mod_df = pd.DataFrame(modified)
print(mod_df)
ind = mod_df.language
width = 0.35
p1 = plt.bar(ind,mod_df.total_count, width)
p2 = plt.bar(ind,mod_df.country_count, width,
bottom=mod_df.total_count)
plt.ylabel("Total count")
plt.xlabel("Languages")
plt.legend((p1[0], p2[0]), ('Total Count', 'Country Count'))
plt.show()
First,modify the dataframe to below dataframe.
language country_count total_count
0 cs 1 432
1 en 2 55
2 es 3 521
3 hi 1 6
4 pt 1 55
This is the plot:
As the value of country count is small, you cannot clearly see the stacked country count.
import seaborn as sns
import matplotlib.pyplot as plt
figure, axis = plt.subplots(1,1,figsize=(10,5))
sns.barplot(x="language",y="count",data=df,ci=None)#,hue='county')
axis.set_title('Score y language and country')
axis.set_ylabel('Count')
axis.set_xlabel("Language")
sns.countplot(x=df.language,data=df)
I have a DataFrame:
wilayah branch Income Januari 2018 Income Januari 2019 Income Febuari 2018 Income Febuari 2019 Income Jan-Feb 2018 Income Jan-Feb 2019
1 sunarto 1000 1500 2000 3000 3333 4431
1 pemabuk 500 700 3000 3000 4333 5431
1 pemalas 2000 2200 4000 3000 5333 6431
1 hasuntato 9000 1200 6000 3000 2222 2121
1 sibodoh 1000 1500 3434 3000 2233 2121
...
My expectation to to create a bar graph where x axis is every name in branch (e.g sunarto, pemabuk, pemalas, etc), and y axis is income.
Let's say I will compare sunarto's income januari 2018 and income januari 2019, pemabuk's income januari 2018 and income januari 2019, and so on (1 name in x axis, 2 values as comparison of two values). Then I will sort values high to low value from Income Jan-Feb 2019 in my bar graph.
I tried:
import matplotlib.pyplot as plt
import pandas as pd
fig, ax = plt.subplots()
ax = df1[["Sunarto","Income Januari 2018", "Income Januari 2019"]].plot(x='branch', kind='bar', color=["g","b"],rot=45)
plt.show()
Consider a groupby aggregation then run DataFrame.plot. Below will line all branches on x-axis with different income columns as color_coded keys in legend.
agg_df = df.groupby('branch').sum()
fig, ax = plt.subplots(figsize=(15,5))
agg_df.plot(kind='bar', edgecolor='w', ax=ax, rot=22, width=0.5, fontsize = 15)
# ADD TITLES AND LABELS
plt.title('Income by Branches, Jan/Feb 2018-2019', weight='bold', size=24)
plt.xlabel('Branch', weight='bold', size=24)
plt.ylabel('Income', weight='bold', size=20)
plt.tight_layout()
plt.show()
plt.clf()
Should you want each separate branch plots on specific columns, iterate off a groupby list:
dfs = df.groupby('branch')
for i,g in dfs:
ord_cols = (pd.melt(g.drop(columns="wilayah"), id_vars = "branch")
.sort_values("value")["variable"].values
)
fig, ax = plt.subplots(figsize=(8,4))
(g.reindex(columns=ord_cols)
.plot(kind='bar', edgecolor='w', ax=ax, rot=0, width=0.5, fontsize = 15)
)
# ADD TITLES AND LABELS
plt.title('Income by {} Branch, Jan/Feb 2018-2019'.format(i),
weight='bold', size=16)
plt.xlabel('Branch', weight='bold', size=16)
plt.ylabel('Income', weight='bold', size=14)
plt.tight_layout()
plt.show()
I wanna make subplots for the following data. I averaged and grouped together.
I wanna make subpolts by country for x-axis resource and y-axis average.
country resource average
india water 76
india soil 45
india tree 60
US water 45
US soil 70
US tree 85
Germany water 76
Germany soil 65
Germany water 56
Grouped = df.groupby(['country','resource'])['TTR in minutes'].agg({'average': 'mean'}).reset_index()
I tried but couldn't plot in subplots
g = df.groupby('country')
fig, axes = plt.subplots(g.ngroups, sharex=True, figsize=(8, 6))
for i, (country, d) in enumerate(g):
ax = d.plot.bar(x='resource', y='average', ax=axes[i], title=country)
ax.legend().remove()
fig.tight_layout()