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What's the difference between:
Maand['P_Sanyo_Gesloten'] Out[119]: Time 2012-08-01 00:00:11 0 2012-08-01 00:05:10 0 2012-08-01 00:10:11 0 2012-08-01 00:20:10 0 2012-08-01 00:25:10 0 2012-08-01 00:30:09 0 2012-08-01 00:40:10 0 2012-08-01 00:50:09 0 2012-08-01 01:05:10 0 2012-08-01 01:10:10 0 2012-08-01 01:15:10 0 2012-08-01 01:25:10 0 2012-08-01 01:30:10 0 2012-08-01 01:35:09 0 2012-08-01 01:40:10 0 ... 2012-08-30 22:35:09 0 2012-08-30 22:45:10 0 2012-08-30 22:50:09 0 2012-08-30 22:55:10 0 2012-08-30 23:00:09 0 2012-08-30 23:05:10 0 2012-08-30 23:10:09 0 2012-08-30 23:15:10 0 2012-08-30 23:20:09 0 2012-08-30 23:25:10 0 2012-08-30 23:35:09 0 2012-08-30 23:40:10 0 2012-08-30 23:45:09 0 2012-08-30 23:50:10 0 2012-08-30 23:55:11 0 Name: P_Sanyo_Gesloten, Length: 7413, dtype: int64 And
Maand[[1]] Out[120]: <class 'pandas.core.frame.DataFrame'> DatetimeIndex: 7413 entries, 2012-08-01 00:00:11 to 2012-08-30 23:55:11 Data columns (total 1 columns): P_Sanyo_Gesloten 7413 non-null values dtypes: int64(1) How can I get data by column-indexnumber? And not by an Index-string?
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You can get the column index from the column name in Pandas using DataFrame. columns. get_loc() method.
You can use the loc and iloc functions to access columns in a Pandas DataFrame. Let's see how. If we wanted to access a certain column in our DataFrame, for example the Grades column, we could simply use the loc function and specify the name of the column in order to retrieve it.
We can use select_if() function to get numeric columns by calling the function with the dataframe name and isnumeric() function that will check for numeric columns.
To select a single column, use square brackets [] with the column name of the column of interest.
One is a column (aka Series), while the other is a DataFrame:
In [1]: df = pd.DataFrame([[1,2], [3,4]], columns=['a', 'b']) In [2]: df Out[2]: a b 0 1 2 1 3 4 The column 'b' (aka Series):
In [3]: df['b'] Out[3]: 0 2 1 4 Name: b, dtype: int64 The subdataframe with columns (position) in [1]:
In [4]: df[[1]] Out[4]: b 0 2 1 4 Note: it's preferable (and less ambiguous) to specify whether you're talking about the column name e.g. ['b'] or the integer location, since sometimes you can have columns named as integers:
In [5]: df.iloc[:, [1]] Out[5]: b 0 2 1 4 In [6]: df.loc[:, ['b']] Out[6]: b 0 2 1 4 In [7]: df.loc[:, 'b'] Out[7]: 0 2 1 4 Name: b, dtype: int64 The following is taken from http://pandas.pydata.org/pandas-docs/dev/indexing.html. There are a few more examples... you have to scroll down a little
In [816]: df1 0 2 4 6 0 0.569605 0.875906 -2.211372 0.974466 2 -2.006747 -0.410001 -0.078638 0.545952 4 -1.219217 -1.226825 0.769804 -1.281247 6 -0.727707 -0.121306 -0.097883 0.695775 8 0.341734 0.959726 -1.110336 -0.619976 10 0.149748 -0.732339 0.687738 0.176444 Select via integer slicing
In [817]: df1.iloc[:3] 0 2 4 6 0 0.569605 0.875906 -2.211372 0.974466 2 -2.006747 -0.410001 -0.078638 0.545952 4 -1.219217 -1.226825 0.769804 -1.281247 In [818]: df1.iloc[1:5,2:4] 4 6 2 -0.078638 0.545952 4 0.769804 -1.281247 6 -0.097883 0.695775 8 -1.110336 -0.619976 Select via integer list
In [819]: df1.iloc[[1,3,5],[1,3]] 2 6 2 -0.410001 0.545952 6 -0.121306 0.695775 10 -0.732339 0.176444 If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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