How to plot different groups of data from a dataframe into a single figure

Question

I have a temperature file with many years temperature records, in a format as below:

2012-04-12,16:13:09,20.6
2012-04-12,17:13:09,20.9
2012-04-12,18:13:09,20.6
2007-05-12,19:13:09,5.4
2007-05-12,20:13:09,20.6
2007-05-12,20:13:09,20.6
2005-08-11,11:13:09,20.6
2005-08-11,11:13:09,17.5
2005-08-13,07:13:09,20.6
2006-04-13,01:13:09,20.6

Every year has different numbers, time of the records, so the pandas datetimeindices are all different.

I want to plot the different year’s data in the same figure for comparing . The X-axis is Jan to Dec, the Y-axis is temperature. How should I go about doing this?

Asked By: wuwucat

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Source

Answer 1

Try:

ax = df1.plot()
df2.plot(ax=ax)

Answered By: Chang She

Answer 2

Chang’s answer shows how to plot a different DataFrame on the same axes.
In this case, all of the data is in the same dataframe, so it’s better to use groupby and unstack.
- Alternatively, pandas.DataFrame.pivot_table can be used.
- dfp = df.pivot_table(index='Month', columns='Year', values='value', aggfunc='mean')
When using pandas.read_csv, names= creates column headers when there are none in the file. The 'date' column must be parsed into datetime64[ns] Dtype so the .dt extractor can be used to extract the month and year.

import pandas as pd

# given the data in a file as shown in the op
df = pd.read_csv('temp.csv', names=['date', 'time', 'value'], parse_dates=['date'])
    
# create additional month and year columns for convenience
df['Year'] = df.date.dt.year
df['Month'] = df.date.dt.month

# groupby the month a year and aggreate mean on the value column
dfg = df.groupby(['Month', 'Year'])['value'].mean().unstack()

# display(dfg)                     
Year        2005  2006       2007  2012
Month                                  
4            NaN  20.6        NaN  20.7
5            NaN   NaN  15.533333   NaN
8      19.566667   NaN        NaN   NaN

Now it’s easy to plot each year as a separate line. The OP only has one observation for each year, so only a marker is displayed.

ax = dfg.plot(figsize=(9, 7), marker='.', xticks=dfg.index)

Answered By: Andy Hayden

Answer 3

If you a running Jupyter/Ipython notebook and having problems using;

ax = df1.plot()

df2.plot(ax=ax)

Run the command inside of the same cell!! It wont, for some reason, work when they are separated into sequential cells. For me at least.

Answered By: Hamish Robertson

Answer 4

To do this for multiple dataframes, you can do a for loop over them:

fig = plt.figure(num=None, figsize=(10, 8))
ax = dict_of_dfs['FOO'].column.plot()
for BAR in dict_of_dfs.keys():
    if BAR == 'FOO':
        pass
    else:
        dict_of_dfs[BAR].column.plot(ax=ax)

This can also be implemented without the if condition:

fig, ax = plt.subplots()
for BAR in dict_of_dfs.keys():
    dict_of_dfs[BAR].plot(ax=ax)

Answered By: adivis12

Answer 5

You can make use of the hue parameter in seaborn. For example:

import seaborn as sns
df = sns.load_dataset('flights')

     year month  passengers
0    1949   Jan         112
1    1949   Feb         118
2    1949   Mar         132
3    1949   Apr         129
4    1949   May         121
..    ...   ...         ...
139  1960   Aug         606
140  1960   Sep         508
141  1960   Oct         461
142  1960   Nov         390
143  1960   Dec         432

sns.lineplot(x='month', y='passengers', hue='year', data=df)

Answered By: Mykola Zotko

How to plot different groups of data from a dataframe into a single figure

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