Pandas Dataframe Groupby Plot

Pandas dataframe groupby plot

Simple plot,

you can use:

df.plot(x='Date',y='adj_close')

Or you can set the index to be Date beforehand, then it's easy to plot the column you want:

df.set_index('Date', inplace=True)
df['adj_close'].plot()

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If you want a chart with one series by ticker on it

You need to groupby before:

df.set_index('Date', inplace=True)
df.groupby('ticker')['adj_close'].plot(legend=True)

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If you want a chart with individual subplots:

grouped = df.groupby('ticker')

ncols=2
nrows = int(np.ceil(grouped.ngroups/ncols))

fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(12,4), sharey=True)

for (key, ax) in zip(grouped.groups.keys(), axes.flatten()):
    grouped.get_group(key).plot(ax=ax)

ax.legend()
plt.show()

How to plot pandas groupby values in a graph

You can use groupby + size and then use Series.plot.bar:

Difference between count and size.

groups = df.groupby(['Gender','Married']).size()
groups.plot.bar()

Another solution is add unstack for reshape or crosstab:

print (df.groupby(['Gender','Married']).size().unstack(fill_value=0))
Married   No  Yes
Gender           
Female    80   31
Male     130  357

df.groupby(['Gender','Married']).size().unstack(fill_value=0).plot.bar()

Or:

pd.crosstab(df['Gender'],df['Married']).plot.bar()

Python Pandas - Group by, then plot by category

You can try the following:

df.groupby([df.time.dt.floor('d'), "category"]).size().unstack().plot()

Explanations:

• First step is to grouby as you mentioned. To do this, we use groupby

• In the groupby, because we need to group the times by days, one solution is to use dt.floor on the time column. We pass the argument "d" for days.

  • Also, to be sure the floor is reachable, the time column must be a time series. If it's not, use pd.to_datetime to convert it with pd.to_datetime(df.time).

• Now we have the group, the size can be easily computed applying the size method.

• The next step is to convert the category column (at this step as index) into columns. Because we groupby by two keys, we can use unstack.

• Finally, call the plot one the dataframe. Because the dataframe is well structured, it works without any arguments (one line is drawn for each column and the index column (time) is used as x-axis.

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Full code + illustration:

# import modules 
import pandas as pd
import matplotlib.pyplot as plt
# (here random is just for creating dummy data)
from random import randint, choice

# Create dummy data
size = 1000
df = pd.DataFrame({
    'time': pd.to_datetime(["2020/01/{} {}:{}".format(randint(1, 31), randint(0,23), randint(0,59)) for _ in range(size)]),
    'text': ['blablabla...' for _ in range(size)],
    'category': [choice(["A", "B", "C"]) for _ in range(size)]
})
print(df)
#                    time          text category
# 0   2020-01-30 23:15:00  blablabla...        C
# 1   2020-01-16 07:06:00  blablabla...        A
# 2   2020-01-03 18:47:00  blablabla...        A
# 3   2020-01-21 15:45:00  blablabla...        A
# 4   2020-01-10 04:11:00  blablabla...        C
# ..                  ...           ...      ...
# 995 2020-01-12 03:03:00  blablabla...        C
# 996 2020-01-08 10:35:00  blablabla...        B
# 997 2020-01-24 20:51:00  blablabla...        C
# 998 2020-01-05 07:39:00  blablabla...        A
# 999 2020-01-26 16:54:00  blablabla...        A

# See size result
print(df.groupby([df.time.dt.floor('d'), "category"]).size())
# time        category
# 2020-01-01  A            6
#             B           18
#             C            7
# 2020-01-02  A           10
#             B            8
#                         ..
# 2020-01-30  B           16
#             C           11
# 2020-01-31  A           14
#             B           17
#             C           11

# See unstack result
print(df.groupby([df.time.dt.floor('d'), "category"]).size().unstack())
# category     A   B   C
# time
# 2020-01-01   6  18   7
# 2020-01-02  10   8  13
# 2020-01-03  11  11  16
# 2020-01-04   9   5  10
# 2020-01-05  13   9  13
# 2020-01-06  11  11  12
# 2020-01-07  13   7   9
# 2020-01-08   5  16  13
# 2020-01-09  15   6  14
# 2020-01-10  10  11   9
# 2020-01-11   7  16  13
# 2020-01-12  12  13  13
# 2020-01-13  12   5   7
# 2020-01-14  11  10  11
# 2020-01-15  13  14  11
# 2020-01-16   9   8  13
# 2020-01-17   8   9   6
# 2020-01-18  12   5  11
# 2020-01-19   7   8  13
# 2020-01-20  12   9   9
# 2020-01-21   9  13  13
# 2020-01-22  14  11  19
# 2020-01-23  14   6  12
# 2020-01-24   7   8   6
# 2020-01-25  10  12  10
# 2020-01-26   8  12   7
# 2020-01-27  18  11   7
# 2020-01-28  15  10   9
# 2020-01-29  12   7  11
# 2020-01-30  12  16  11
# 2020-01-31  14  17  11

# Perform plot
df.groupby([df.time.dt.floor('d'), "category"]).size().unstack().plot()
plt.show()

output:

Python Groupby and plotting of data

Do you want a bar plot? then

Edit: thanks to not_speshal for pointing out temperature column

(df.assign(temp=lambda x: x["Temperature"].str.extract("(\d+)").astype(float))
.groupby("Date").agg({"Total":"sum","temp":"first"})[["Total","temp"]].plot(kind="bar"))

pandas groupby objects, combining and plotting

About the second part of your question, you can use string manipulation.

If I understand correctly you can use this:

a = data['bin']
a1 = a.astype(str).str.strip('([])').str.split(',').str[0].astype(float) 
a2 = a.astype(str).str.strip('([])').str.split(',').str[1].astype(float)

data['bin_center'] = (a1+a2)/2
g = data.groupby('bin_center')['sepal width (cm)'].agg(['count', 'mean', 'std'])

plt.plot(g.index, g['mean'])

by the way, if you don't relly want the bin center, and you want to see the plot with the bins

you can use dataframe plot:

g = data.groupby('bin')['sepal width (cm)'].agg(['count', 'mean', 'std'])
print(g)
g['mean'].plot()

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