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| pyarrow.NA |
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| pyarrow.Scalar |
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| pyarrow.BooleanScalar |
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| pyarrow.Int8Scalar |
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| pyarrow.FloatScalar |
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| pyarrow.DoubleScalar |
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| pyarrow.BinaryScalar |
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| pyarrow.StructScalar |
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| pyarrow.UnionScalar |
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| pyarrow.compute.power |
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| pyarrow.compute.tan_checked |
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| pyarrow.compute.equal |
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| pyarrow.compute.greater |
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| pyarrow.compute.less_equal |
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| pyarrow.compute.not_equal |
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| pyarrow.compute.ascii_is_upper |
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| pyarrow.compute.utf8_is_alnum |
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| pyarrow.compute.utf8_is_alpha |
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| pyarrow.compute.utf8_is_upper |
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| pyarrow.compute.string_is_ascii |
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| pyarrow.compute.split_pattern |
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| pyarrow.compute.split_pattern_regex |
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| pyarrow.compute.ascii_split_whitespace |
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| pyarrow.compute.extract_regex |
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| pyarrow.compute.binary_join |
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| pyarrow.compute.ascii_lower |
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| pyarrow.compute.ascii_reverse |
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| pyarrow.compute.ascii_rpad |
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| pyarrow.compute.binary_length |
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| pyarrow.compute.binary_replace_slice |
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| pyarrow.compute.replace_substring |
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| pyarrow.compute.replace_substring_regex |
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| pyarrow.compute.utf8_center |
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| pyarrow.compute.utf8_length |
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| pyarrow.compute.utf8_upper |
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| pyarrow.compute.count_substring |
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| pyarrow.compute.count_substring_regex |
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| pyarrow.compute.ends_with |
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| pyarrow.compute.find_substring |
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| pyarrow.compute.find_substring_regex |
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| pyarrow.compute.index_in |
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| pyarrow.compute.is_in |
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| pyarrow.compute.match_like |
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| pyarrow.compute.starts_with |
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| pyarrow.compute.cast |
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| pyarrow.compute.strptime |
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| pyarrow.compute.replace_with_mask |
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| pyarrow.compute.filter |
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| pyarrow.compute.take |
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| pyarrow.compute.value_counts |
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| pyarrow.concat_arrays |
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| pyarrow.RecordBatch |
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| pyarrow.Tensor |
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| |
| <div class="section" id="timestamps"> |
| <h1>Timestamps<a class="headerlink" href="#timestamps" title="Permalink to this headline">¶</a></h1> |
| <div class="section" id="arrow-pandas-timestamps"> |
| <h2>Arrow/Pandas Timestamps<a class="headerlink" href="#arrow-pandas-timestamps" title="Permalink to this headline">¶</a></h2> |
| <p>Arrow timestamps are stored as a 64-bit integer with column metadata to |
| associate a time unit (e.g. milliseconds, microseconds, or nanoseconds), and an |
| optional time zone. Pandas (<cite>Timestamp</cite>) uses a 64-bit integer representing |
| nanoseconds and an optional time zone. |
| Python/Pandas timestamp types without a associated time zone are referred to as |
| “Time Zone Naive”. Python/Pandas timestamp types with an associated time zone are |
| referred to as “Time Zone Aware”.</p> |
| </div> |
| <div class="section" id="timestamp-conversions"> |
| <h2>Timestamp Conversions<a class="headerlink" href="#timestamp-conversions" title="Permalink to this headline">¶</a></h2> |
| <div class="section" id="pandas-arrow-spark"> |
| <h3>Pandas/Arrow ⇄ Spark<a class="headerlink" href="#pandas-arrow-spark" title="Permalink to this headline">¶</a></h3> |
| <p>Spark stores timestamps as 64-bit integers representing microseconds since |
| the UNIX epoch. It does not store any metadata about time zones with its |
| timestamps.</p> |
| <p>Spark interprets timestamps with the <em>session local time zone</em>, (i.e. |
| <code class="docutils literal notranslate"><span class="pre">spark.sql.session.timeZone</span></code>). If that time zone is undefined, Spark turns to |
| the default system time zone. For simplicity’s sake below, the session |
| local time zone is always defined.</p> |
| <p>This implies a few things when round-tripping timestamps:</p> |
| <ol class="arabic simple"> |
| <li><p>Timezone information is lost (all timestamps that result from |
| converting from spark to arrow/pandas are “time zone naive”).</p></li> |
| <li><p>Timestamps are truncated to microseconds.</p></li> |
| <li><p>The session time zone might have unintuitive impacts on |
| translation of timestamp values.</p></li> |
| </ol> |
| <div class="section" id="spark-to-pandas-through-apache-arrow"> |
| <h4>Spark to Pandas (through Apache Arrow)<a class="headerlink" href="#spark-to-pandas-through-apache-arrow" title="Permalink to this headline">¶</a></h4> |
| <p>The following cases assume the Spark configuration |
| <code class="docutils literal notranslate"><span class="pre">spark.sql.execution.arrow.enabled</span></code> is set to <code class="docutils literal notranslate"><span class="pre">"true"</span></code>.</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">pdf</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'naive'</span><span class="p">:</span> <span class="p">[</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2019</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)],</span> |
| <span class="gp">... </span> <span class="s1">'aware'</span><span class="p">:</span> <span class="p">[</span><span class="n">Timestamp</span><span class="p">(</span><span class="n">year</span><span class="o">=</span><span class="mi">2019</span><span class="p">,</span> <span class="n">month</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">day</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> |
| <span class="gp">... </span> <span class="n">nanosecond</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span> <span class="n">tz</span><span class="o">=</span><span class="n">timezone</span><span class="p">(</span><span class="n">timedelta</span><span class="p">(</span><span class="n">hours</span><span class="o">=-</span><span class="mi">8</span><span class="p">)))]})</span> |
| <span class="gp">>>> </span><span class="n">pdf</span> |
| <span class="go"> naive aware</span> |
| <span class="go"> 0 2018-10-01 2018-10-01 00:00:00.000000500-08:00</span> |
| |
| <span class="gp">>>> </span><span class="n">spark</span><span class="o">.</span><span class="n">conf</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="s2">"spark.sql.session.timeZone"</span><span class="p">,</span> <span class="s2">"UTC"</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">utc_df</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span><span class="n">pdf</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">utf_df</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> |
| <span class="go">+-------------------+-------------------+</span> |
| <span class="go">| naive| aware|</span> |
| <span class="go">+-------------------+-------------------+</span> |
| <span class="go">|2019-01-01 00:00:00|2019-01-01 08:00:00|</span> |
| <span class="go">+-------------------+-------------------+</span> |
| </pre></div> |
| </div> |
| <p>Note that conversion of the aware timestamp is shifted to reflect the time |
| assuming UTC (it represents the same instant in time). For naive |
| timestamps, Spark treats them as being in the system local |
| time zone and converts them UTC. Recall that internally, the schema |
| for spark dataframe’s does not store any time zone information with |
| timestamps.</p> |
| <p>Now if the session time zone is set to US Pacific Time (PST) we don’t |
| see any shift in the display of the aware time zone (it |
| still represents the same instant in time):</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">spark</span><span class="o">.</span><span class="n">conf</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="s2">"spark.sql.session.timeZone"</span><span class="p">,</span> <span class="s2">"US/Pacific"</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">pst_df</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span><span class="n">pdf</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">pst_df</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> |
| <span class="go">+-------------------+-------------------+</span> |
| <span class="go">| naive| aware|</span> |
| <span class="go">+-------------------+-------------------+</span> |
| <span class="go">|2019-01-01 00:00:00|2019-01-01 00:00:00|</span> |
| <span class="go">+-------------------+-------------------+</span> |
| </pre></div> |
| </div> |
| <p>Looking again at utc_df.show() we see one of the impacts of the session time |
| zone. The naive timestamp was initially converted assuming UTC, the instant it |
| reflects is actually earlier than the naive time zone from the PST converted |
| data frame:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">utc_df</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> |
| <span class="go">+-------------------+-------------------+</span> |
| <span class="go">| naive| aware|</span> |
| <span class="go">+-------------------+-------------------+</span> |
| <span class="go">|2018-12-31 16:00:00|2019-01-01 00:00:00|</span> |
| <span class="go">+-------------------+-------------------+</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="spark-to-pandas"> |
| <h4>Spark to Pandas<a class="headerlink" href="#spark-to-pandas" title="Permalink to this headline">¶</a></h4> |
| <p>We can observe what happens when converting back to Arrow/Pandas. Assuming the |
| session time zone is still PST:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">pst_df</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> |
| <span class="go">+-------------------+-------------------+</span> |
| <span class="go">| naive| aware|</span> |
| <span class="go">+-------------------+-------------------+</span> |
| <span class="go">|2019-01-01 00:00:00|2019-01-01 00:00:00|</span> |
| <span class="go">+-------------------+-------------------+</span> |
| |
| |
| <span class="go"> >>> pst_df.toPandas()</span> |
| <span class="go"> naive aware</span> |
| <span class="go"> 0 2019-01-01 2019-01-01</span> |
| <span class="go"> >>> pst_df.toPandas().info()</span> |
| <span class="go"> <class 'pandas.core.frame.DataFrame'></span> |
| <span class="go"> RangeIndex: 1 entries, 0 to 0</span> |
| <span class="go"> Data columns (total 2 columns):</span> |
| <span class="go"> naive 1 non-null datetime64[ns]</span> |
| <span class="go"> aware 1 non-null datetime64[ns]</span> |
| <span class="go"> dtypes: datetime64[ns](2)</span> |
| <span class="go"> memory usage: 96.0 bytes</span> |
| </pre></div> |
| </div> |
| <p>Notice that, in addition to being a “time zone naive” timestamp, the ‘aware’ |
| value will now differ when converting to an epoch offset. Spark does the conversion |
| by first converting to the session time zone (or system local time zone if |
| session time zones isn’t set) and then localizes to remove the time zone |
| information. This results in the timestamp being 8 hours before the original |
| time:</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">pst_df</span><span class="o">.</span><span class="n">toPandas</span><span class="p">()[</span><span class="s1">'aware'</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> |
| <span class="go">Timestamp('2019-01-01 00:00:00')</span> |
| <span class="gp">>>> </span><span class="n">pdf</span><span class="p">[</span><span class="s1">'aware'</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> |
| <span class="go">Timestamp('2019-01-01 00:00:00.000000500-0800', tz='UTC-08:00')</span> |
| <span class="gp">>>> </span><span class="p">(</span><span class="n">pst_df</span><span class="o">.</span><span class="n">toPandas</span><span class="p">()[</span><span class="s1">'aware'</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">timestamp</span><span class="p">()</span><span class="o">-</span><span class="n">pdf</span><span class="p">[</span><span class="s1">'aware'</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">timestamp</span><span class="p">())</span><span class="o">/</span><span class="mi">3600</span> |
| <span class="go">-8.0</span> |
| </pre></div> |
| </div> |
| <p>The same type of conversion happens with the data frame converted while |
| the session time zone was UTC. In this case both naive and aware |
| represent different instants in time (the naive instant is due to |
| the change in session time zone between creating data frames):</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">utc_df</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> |
| <span class="go">+-------------------+-------------------+</span> |
| <span class="go">| naive| aware|</span> |
| <span class="go">+-------------------+-------------------+</span> |
| <span class="go">|2018-12-31 16:00:00|2019-01-01 00:00:00|</span> |
| <span class="go">+-------------------+-------------------+</span> |
| |
| <span class="gp">>>> </span><span class="n">utc_df</span><span class="o">.</span><span class="n">toPandas</span><span class="p">()</span> |
| <span class="go">naive aware</span> |
| <span class="go">0 2018-12-31 16:00:00 2019-01-01</span> |
| </pre></div> |
| </div> |
| <p>Note that the surprising shift for aware doesn’t happen |
| when the session time zone is UTC (but the timestamps |
| still become “time zone naive”):</p> |
| <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">spark</span><span class="o">.</span><span class="n">conf</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="s2">"spark.sql.session.timeZone"</span><span class="p">,</span> <span class="s2">"UTC"</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">pst_df</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> |
| <span class="go">+-------------------+-------------------+</span> |
| <span class="go">| naive| aware|</span> |
| <span class="go">+-------------------+-------------------+</span> |
| <span class="go">|2019-01-01 08:00:00|2019-01-01 08:00:00|</span> |
| <span class="go">+-------------------+-------------------+</span> |
| |
| <span class="gp">>>> </span><span class="n">pst_df</span><span class="o">.</span><span class="n">toPandas</span><span class="p">()[</span><span class="s1">'aware'</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> |
| <span class="go">Timestamp('2019-01-01 08:00:00')</span> |
| <span class="gp">>>> </span><span class="n">pdf</span><span class="p">[</span><span class="s1">'aware'</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> |
| <span class="go">Timestamp('2019-01-01 00:00:00.000000500-0800', tz='UTC-08:00')</span> |
| <span class="gp">>>> </span><span class="p">(</span><span class="n">pst_df</span><span class="o">.</span><span class="n">toPandas</span><span class="p">()[</span><span class="s1">'aware'</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">timestamp</span><span class="p">()</span><span class="o">-</span><span class="n">pdf</span><span class="p">[</span><span class="s1">'aware'</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">timestamp</span><span class="p">())</span><span class="o">/</span><span class="mi">3600</span> |
| <span class="go">0.0</span> |
| </pre></div> |
| </div> |
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