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How to convert pandas dataframe to 3D Panel
How to merge two dictionaries in a single expression?How do I check if a list is empty?How do I check whether a file exists without exceptions?How do I list all files of a directory?Selecting multiple columns in a pandas dataframeRenaming columns in pandasDelete column from pandas DataFrame“Large data” work flows using pandasHow to iterate over rows in a DataFrame in Pandas?Select rows from a DataFrame based on values in a column in pandas
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;
I am reading data from a CSV file which contains weather data for a network of buoys situated off the coast of Ireland. it is a time series dataset with hourly readings for each buoy. I want to create a 3D structure where there is a dataframe for each buoy, containing the columns of the weather conditions, indexed by the date and time.
I would like to be able to access the data via the following syntax:
df['column']['anotherColumn']
I'm aware that pandas has a deprecated Panel class, but I can't work out how to do this otherwise.
Any help would be appreciated, thanks!
python pandas
edited Mar 27 at 0:18
Xukrao
2,9754 gold badges10 silver badges34 bronze badges
asked Mar 26 at 21:16
C. DunphC. Dunph
133 bronze badges
add a comment |
I am reading data from a CSV file which contains weather data for a network of buoys situated off the coast of Ireland. it is a time series dataset with hourly readings for each buoy. I want to create a 3D structure where there is a dataframe for each buoy, containing the columns of the weather conditions, indexed by the date and time.
I would like to be able to access the data via the following syntax:
df['column']['anotherColumn']
I'm aware that pandas has a deprecated Panel class, but I can't work out how to do this otherwise.
Any help would be appreciated, thanks!
python pandas
edited Mar 27 at 0:18
Xukrao
2,9754 gold badges10 silver badges34 bronze badges
asked Mar 26 at 21:16
C. DunphC. Dunph
133 bronze badges
2
Use a multi level index?
– Niels Henkens
Mar 26 at 21:18
I'll look into it. I'm new to pandas so yet to work out the finer details, thanks.
– C. Dunph
Mar 26 at 21:20
add a comment |
I am reading data from a CSV file which contains weather data for a network of buoys situated off the coast of Ireland. it is a time series dataset with hourly readings for each buoy. I want to create a 3D structure where there is a dataframe for each buoy, containing the columns of the weather conditions, indexed by the date and time.
I would like to be able to access the data via the following syntax:
df['column']['anotherColumn']
I'm aware that pandas has a deprecated Panel class, but I can't work out how to do this otherwise.
Any help would be appreciated, thanks!
python pandas
edited Mar 27 at 0:18
Xukrao
2,9754 gold badges10 silver badges34 bronze badges
asked Mar 26 at 21:16
C. DunphC. Dunph
133 bronze badges
I am reading data from a CSV file which contains weather data for a network of buoys situated off the coast of Ireland. it is a time series dataset with hourly readings for each buoy. I want to create a 3D structure where there is a dataframe for each buoy, containing the columns of the weather conditions, indexed by the date and time.
I would like to be able to access the data via the following syntax:
df['column']['anotherColumn']
I'm aware that pandas has a deprecated Panel class, but I can't work out how to do this otherwise.
Any help would be appreciated, thanks!
python pandas
python pandas
edited Mar 27 at 0:18
Xukrao
2,9754 gold badges10 silver badges34 bronze badges
asked Mar 26 at 21:16
C. DunphC. Dunph
133 bronze badges
edited Mar 27 at 0:18
Xukrao
2,9754 gold badges10 silver badges34 bronze badges
asked Mar 26 at 21:16
C. DunphC. Dunph
133 bronze badges
edited Mar 27 at 0:18
Xukrao
2,9754 gold badges10 silver badges34 bronze badges
edited Mar 27 at 0:18
Xukrao
2,9754 gold badges10 silver badges34 bronze badges
edited Mar 27 at 0:18
Xukrao
2,9754 gold badges10 silver badges34 bronze badges
2,9754 gold badges10 silver badges34 bronze badges
asked Mar 26 at 21:16
C. DunphC. Dunph
133 bronze badges
asked Mar 26 at 21:16
C. DunphC. Dunph
133 bronze badges
asked Mar 26 at 21:16
C. DunphC. Dunph
133 bronze badges
133 bronze badges
2
Use a multi level index?
– Niels Henkens
Mar 26 at 21:18
I'll look into it. I'm new to pandas so yet to work out the finer details, thanks.
– C. Dunph
Mar 26 at 21:20
add a comment |
2
Use a multi level index?
– Niels Henkens
Mar 26 at 21:18
I'll look into it. I'm new to pandas so yet to work out the finer details, thanks.
– C. Dunph
Mar 26 at 21:20
2
2
Use a multi level index?
– Niels Henkens
Mar 26 at 21:18
Use a multi level index?
– Niels Henkens
Mar 26 at 21:18
I'll look into it. I'm new to pandas so yet to work out the finer details, thanks.
– C. Dunph
Mar 26 at 21:20
I'll look into it. I'm new to pandas so yet to work out the finer details, thanks.
– C. Dunph
Mar 26 at 21:20
add a comment |
1 Answer
1
active
oldest
votes
The pandas Panel was deprecated in favour of DataFrame with multi-level index. To quote from the pandas documentation:
Hierarchical / Multi-level indexing is very exciting as it opens the
door to some quite sophisticated data analysis and manipulation,
especially for working with higher dimensional data. In essence, it
enables you to store and manipulate data with an arbitrary number of
dimensions in lower dimensional data structures like Series (1d) and
DataFrame (2d).
Here's a quick example of a DataFrame with MultiIndex used to represent a three-dimensional data set:
In [1]: multi_index = pd.MultiIndex.from_arrays([
...: ['buoy1', 'buoy1', 'buoy2', 'buoy2', 'buoy3', 'buoy3', 'buoy4', 'buoy4'],
...: ['wind', 'water', 'wind', 'water', 'wind', 'water', 'wind', 'water'],
...: ])
In [2]: df = pd.DataFrame(np.random.randn(3, 8), columns=multi_index)
In [3]: df
Out[3]:
buoy1 buoy2 buoy3 buoy4
wind water wind water wind water wind water
0 1.082442 -0.148975 -0.372837 0.075599 1.681150 0.910194 0.157064 0.183764
1 -0.019759 1.782505 -1.092751 0.324313 -2.217671 0.349224 1.085250 -0.715607
2 -1.308382 -0.994506 -0.306874 0.517858 1.356037 -0.024291 0.085105 -0.073061
Subsequently you can slice down to a 2D section of your data set like so:
In [4]: df['buoy3']
Out[4]:
wind water
0 1.681150 0.910194
1 -2.217671 0.349224
2 1.356037 -0.024291
And you can slice down to a 1D section (i.e. single column) of your data set like so:
In [5]: df['buoy3']['water']
Out[5]:
0 0.910194
1 0.349224
2 -0.024291
Name: water, dtype: float64
answered Mar 27 at 0:06
XukraoXukrao
2,9754 gold badges10 silver badges34 bronze badges
add a comment |
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1 Answer
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1 Answer
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votes
The pandas Panel was deprecated in favour of DataFrame with multi-level index. To quote from the pandas documentation:
Hierarchical / Multi-level indexing is very exciting as it opens the
door to some quite sophisticated data analysis and manipulation,
especially for working with higher dimensional data. In essence, it
enables you to store and manipulate data with an arbitrary number of
dimensions in lower dimensional data structures like Series (1d) and
DataFrame (2d).
Here's a quick example of a DataFrame with MultiIndex used to represent a three-dimensional data set:
In [1]: multi_index = pd.MultiIndex.from_arrays([
...: ['buoy1', 'buoy1', 'buoy2', 'buoy2', 'buoy3', 'buoy3', 'buoy4', 'buoy4'],
...: ['wind', 'water', 'wind', 'water', 'wind', 'water', 'wind', 'water'],
...: ])
In [2]: df = pd.DataFrame(np.random.randn(3, 8), columns=multi_index)
In [3]: df
Out[3]:
buoy1 buoy2 buoy3 buoy4
wind water wind water wind water wind water
0 1.082442 -0.148975 -0.372837 0.075599 1.681150 0.910194 0.157064 0.183764
1 -0.019759 1.782505 -1.092751 0.324313 -2.217671 0.349224 1.085250 -0.715607
2 -1.308382 -0.994506 -0.306874 0.517858 1.356037 -0.024291 0.085105 -0.073061
Subsequently you can slice down to a 2D section of your data set like so:
In [4]: df['buoy3']
Out[4]:
wind water
0 1.681150 0.910194
1 -2.217671 0.349224
2 1.356037 -0.024291
And you can slice down to a 1D section (i.e. single column) of your data set like so:
In [5]: df['buoy3']['water']
Out[5]:
0 0.910194
1 0.349224
2 -0.024291
Name: water, dtype: float64
answered Mar 27 at 0:06
XukraoXukrao
2,9754 gold badges10 silver badges34 bronze badges
add a comment |
The pandas Panel was deprecated in favour of DataFrame with multi-level index. To quote from the pandas documentation:
Hierarchical / Multi-level indexing is very exciting as it opens the
door to some quite sophisticated data analysis and manipulation,
especially for working with higher dimensional data. In essence, it
enables you to store and manipulate data with an arbitrary number of
dimensions in lower dimensional data structures like Series (1d) and
DataFrame (2d).
Here's a quick example of a DataFrame with MultiIndex used to represent a three-dimensional data set:
In [1]: multi_index = pd.MultiIndex.from_arrays([
...: ['buoy1', 'buoy1', 'buoy2', 'buoy2', 'buoy3', 'buoy3', 'buoy4', 'buoy4'],
...: ['wind', 'water', 'wind', 'water', 'wind', 'water', 'wind', 'water'],
...: ])
In [2]: df = pd.DataFrame(np.random.randn(3, 8), columns=multi_index)
In [3]: df
Out[3]:
buoy1 buoy2 buoy3 buoy4
wind water wind water wind water wind water
0 1.082442 -0.148975 -0.372837 0.075599 1.681150 0.910194 0.157064 0.183764
1 -0.019759 1.782505 -1.092751 0.324313 -2.217671 0.349224 1.085250 -0.715607
2 -1.308382 -0.994506 -0.306874 0.517858 1.356037 -0.024291 0.085105 -0.073061
Subsequently you can slice down to a 2D section of your data set like so:
In [4]: df['buoy3']
Out[4]:
wind water
0 1.681150 0.910194
1 -2.217671 0.349224
2 1.356037 -0.024291
And you can slice down to a 1D section (i.e. single column) of your data set like so:
In [5]: df['buoy3']['water']
Out[5]:
0 0.910194
1 0.349224
2 -0.024291
Name: water, dtype: float64
answered Mar 27 at 0:06
XukraoXukrao
2,9754 gold badges10 silver badges34 bronze badges
add a comment |
The pandas Panel was deprecated in favour of DataFrame with multi-level index. To quote from the pandas documentation:
Hierarchical / Multi-level indexing is very exciting as it opens the
door to some quite sophisticated data analysis and manipulation,
especially for working with higher dimensional data. In essence, it
enables you to store and manipulate data with an arbitrary number of
dimensions in lower dimensional data structures like Series (1d) and
DataFrame (2d).
Here's a quick example of a DataFrame with MultiIndex used to represent a three-dimensional data set:
In [1]: multi_index = pd.MultiIndex.from_arrays([
...: ['buoy1', 'buoy1', 'buoy2', 'buoy2', 'buoy3', 'buoy3', 'buoy4', 'buoy4'],
...: ['wind', 'water', 'wind', 'water', 'wind', 'water', 'wind', 'water'],
...: ])
In [2]: df = pd.DataFrame(np.random.randn(3, 8), columns=multi_index)
In [3]: df
Out[3]:
buoy1 buoy2 buoy3 buoy4
wind water wind water wind water wind water
0 1.082442 -0.148975 -0.372837 0.075599 1.681150 0.910194 0.157064 0.183764
1 -0.019759 1.782505 -1.092751 0.324313 -2.217671 0.349224 1.085250 -0.715607
2 -1.308382 -0.994506 -0.306874 0.517858 1.356037 -0.024291 0.085105 -0.073061
Subsequently you can slice down to a 2D section of your data set like so:
In [4]: df['buoy3']
Out[4]:
wind water
0 1.681150 0.910194
1 -2.217671 0.349224
2 1.356037 -0.024291
And you can slice down to a 1D section (i.e. single column) of your data set like so:
In [5]: df['buoy3']['water']
Out[5]:
0 0.910194
1 0.349224
2 -0.024291
Name: water, dtype: float64
answered Mar 27 at 0:06
XukraoXukrao
2,9754 gold badges10 silver badges34 bronze badges
The pandas Panel was deprecated in favour of DataFrame with multi-level index. To quote from the pandas documentation:
Hierarchical / Multi-level indexing is very exciting as it opens the
door to some quite sophisticated data analysis and manipulation,
especially for working with higher dimensional data. In essence, it
enables you to store and manipulate data with an arbitrary number of
dimensions in lower dimensional data structures like Series (1d) and
DataFrame (2d).
Here's a quick example of a DataFrame with MultiIndex used to represent a three-dimensional data set:
In [1]: multi_index = pd.MultiIndex.from_arrays([
...: ['buoy1', 'buoy1', 'buoy2', 'buoy2', 'buoy3', 'buoy3', 'buoy4', 'buoy4'],
...: ['wind', 'water', 'wind', 'water', 'wind', 'water', 'wind', 'water'],
...: ])
In [2]: df = pd.DataFrame(np.random.randn(3, 8), columns=multi_index)
In [3]: df
Out[3]:
buoy1 buoy2 buoy3 buoy4
wind water wind water wind water wind water
0 1.082442 -0.148975 -0.372837 0.075599 1.681150 0.910194 0.157064 0.183764
1 -0.019759 1.782505 -1.092751 0.324313 -2.217671 0.349224 1.085250 -0.715607
2 -1.308382 -0.994506 -0.306874 0.517858 1.356037 -0.024291 0.085105 -0.073061
Subsequently you can slice down to a 2D section of your data set like so:
In [4]: df['buoy3']
Out[4]:
wind water
0 1.681150 0.910194
1 -2.217671 0.349224
2 1.356037 -0.024291
And you can slice down to a 1D section (i.e. single column) of your data set like so:
In [5]: df['buoy3']['water']
Out[5]:
0 0.910194
1 0.349224
2 -0.024291
Name: water, dtype: float64
answered Mar 27 at 0:06
XukraoXukrao
2,9754 gold badges10 silver badges34 bronze badges
edited Mar 27 at 0:15
answered Mar 27 at 0:06
XukraoXukrao
2,9754 gold badges10 silver badges34 bronze badges
answered Mar 27 at 0:06
XukraoXukrao
2,9754 gold badges10 silver badges34 bronze badges
answered Mar 27 at 0:06
XukraoXukrao
2,9754 gold badges10 silver badges34 bronze badges
2,9754 gold badges10 silver badges34 bronze badges
add a comment |
add a comment |
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2
Use a multi level index?
– Niels Henkens
Mar 26 at 21:18
I'll look into it. I'm new to pandas so yet to work out the finer details, thanks.
– C. Dunph
Mar 26 at 21:20