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    <p>You can adjust pandas print options with <code>set_printoptions</code>.</p> <pre><code>In [3]: df.describe() Out[3]: &lt;class 'pandas.core.frame.DataFrame'&gt; Index: 8 entries, count to max Data columns: x1 8 non-null values x2 8 non-null values x3 8 non-null values x4 8 non-null values x5 8 non-null values x6 8 non-null values x7 8 non-null values dtypes: float64(7) In [4]: pd.set_printoptions(precision=2) In [5]: df.describe() Out[5]: x1 x2 x3 x4 x5 x6 x7 count 8.0 8.0 8.0 8.0 8.0 8.0 8.0 mean 69024.5 69025.5 69026.5 69027.5 69028.5 69029.5 69030.5 std 17.1 17.1 17.1 17.1 17.1 17.1 17.1 min 69000.0 69001.0 69002.0 69003.0 69004.0 69005.0 69006.0 25% 69012.2 69013.2 69014.2 69015.2 69016.2 69017.2 69018.2 50% 69024.5 69025.5 69026.5 69027.5 69028.5 69029.5 69030.5 75% 69036.8 69037.8 69038.8 69039.8 69040.8 69041.8 69042.8 max 69049.0 69050.0 69051.0 69052.0 69053.0 69054.0 69055.0 </code></pre> <p>However this will not work in all cases as pandas detects your console width and it will only use <code>to_string</code> if the output fits in the console (see the docstring of <code>set_printoptions</code>). In this case you can explicitly call <code>to_string</code> as answered by <a href="https://stackoverflow.com/a/11707706/1301710">BrenBarn</a>.</p> <p><strong>Update</strong></p> <p>With version 0.10 the way wide dataframes are printed <a href="http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#wide-dataframe-print" rel="nofollow noreferrer">changed</a>:</p> <pre><code>In [3]: df.describe() Out[3]: x1 x2 x3 x4 x5 \ count 8.000000 8.000000 8.000000 8.000000 8.000000 mean 59832.361578 27356.711336 49317.281222 51214.837838 51254.839690 std 22600.723536 26867.192716 28071.737509 21012.422793 33831.515761 min 31906.695474 1648.359160 56.378115 16278.322271 43.745574 25% 45264.625201 12799.540572 41429.628749 40374.273582 29789.643875 50% 56340.214856 18666.456293 51995.661512 54894.562656 47667.684422 75% 75587.003417 31375.610322 61069.190523 67811.893435 76014.884048 max 98136.474782 84544.484627 91743.983895 75154.587156 99012.695717 x6 x7 count 8.000000 8.000000 mean 41863.000717 33950.235126 std 38709.468281 29075.745673 min 3590.990740 1833.464154 25% 15145.759625 6879.523949 50% 22139.243042 33706.029946 75% 72038.983496 51449.893980 max 98601.190488 83309.051963 </code></pre> <p>Further more the API for setting pandas options changed:</p> <pre><code>In [4]: pd.set_option('display.precision', 2) In [5]: df.describe() Out[5]: x1 x2 x3 x4 x5 x6 x7 count 8.0 8.0 8.0 8.0 8.0 8.0 8.0 mean 59832.4 27356.7 49317.3 51214.8 51254.8 41863.0 33950.2 std 22600.7 26867.2 28071.7 21012.4 33831.5 38709.5 29075.7 min 31906.7 1648.4 56.4 16278.3 43.7 3591.0 1833.5 25% 45264.6 12799.5 41429.6 40374.3 29789.6 15145.8 6879.5 50% 56340.2 18666.5 51995.7 54894.6 47667.7 22139.2 33706.0 75% 75587.0 31375.6 61069.2 67811.9 76014.9 72039.0 51449.9 max 98136.5 84544.5 91744.0 75154.6 99012.7 98601.2 83309.1 </code></pre>
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    1. COI prefer to use the max_columns method mentioned by lodagro, but I'm glad you mentioned the precision keyword since that will help clean up the stats that are displayed. Thanks!
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