From 0fa07e7359fb3cfd8fc968603b8f44c1c6c42428 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jure=20=C5=A0orn?= Date: Sat, 27 Jun 2020 15:31:12 +0200 Subject: [PATCH] Pandas --- README.md | 14 +++++++------- index.html | 14 +++++++------- 2 files changed, 14 insertions(+), 14 deletions(-) diff --git a/README.md b/README.md index fce8fde..8434cf5 100644 --- a/README.md +++ b/README.md @@ -3181,8 +3181,8 @@ b 3 4 ```python = .set_index(column_key) # Replaces row keys with values from a column. = .reset_index() # Moves row keys to their own column. - = .transpose() # Rotates the table. - = .melt(id_vars=column_key/s) # Melts on columns. + = .filter('', axis=1) # Only keeps columns whose key matches the regex. + = .melt(id_vars=column_key/s) # Convers DF from wide to long format. ``` #### Merge, Join, Concat: @@ -3387,12 +3387,12 @@ def scrape_data(): covid = pd.read_csv('https://covid.ourworldindata.org/data/owid-covid-data.csv', usecols=['date', 'total_cases']) covid = covid.groupby('date').sum() - dow, gold, btc = [scrape_yahoo(id_) for id_ in ('^DJI', 'GC=F', 'BTC-USD')] - dow.name, gold.name, btc.name = 'Dow Jones', 'Gold', 'Bitcoin' - return covid, dow, gold, btc + dow, gold, bitcoin = [scrape_yahoo(id_) for id_ in ('^DJI', 'GC=F', 'BTC-USD')] + dow.name, gold.name, bitcoin.name = 'Dow Jones', 'Gold', 'Bitcoin' + return covid, dow, gold, bitcoin -def wrangle_data(covid, dow, gold, btc): - df = pandas.concat([covid, dow, gold, btc], axis=1) +def wrangle_data(covid, dow, gold, bitcoin): + df = pandas.concat([covid, dow, gold, bitcoin], axis=1) df = df.loc['2020-02-23':].iloc[:-2] df = df.interpolate() df.iloc[:, 1:] = df.rolling(10, min_periods=1, center=True).mean().iloc[:, 1:] diff --git a/index.html b/index.html index 5f081d8..7cf350e 100644 --- a/index.html +++ b/index.html @@ -2707,8 +2707,8 @@ b 3 4 
<DF>    = <DF>.set_index(column_key)          # Replaces row keys with values from a column.
 <DF>    = <DF>.reset_index()                  # Moves row keys to their own column.
-<DF>    = <DF>.transpose()                    # Rotates the table.
-<DF>    = <DF>.melt(id_vars=column_key/s)     # Melts on columns.
+<DF>    = <DF>.filter('<regex>', axis=1)      # Only keeps columns whose key matches the regex.
+<DF>    = <DF>.melt(id_vars=column_key/s)     # Convers DF from wide to long format.

Merge, Join, Concat:

>>> l = DataFrame([[1, 2], [3, 4]], index=['a', 'b'], columns=['x', 'y'])
    x  y 
@@ -2871,12 +2871,12 @@ plotly.express.line(df, x='Date', y='https://covid.ourworldindata.org/data/owid-covid-data.csv', 
                         usecols=['date', 'total_cases'])
     covid = covid.groupby('date').sum()
-    dow, gold, btc = [scrape_yahoo(id_) for id_ in ('^DJI', 'GC=F', 'BTC-USD')]
-    dow.name, gold.name, btc.name = 'Dow Jones', 'Gold', 'Bitcoin'
-    return covid, dow, gold, btc
+    dow, gold, bitcoin = [scrape_yahoo(id_) for id_ in ('^DJI', 'GC=F', 'BTC-USD')]
+    dow.name, gold.name, bitcoin.name = 'Dow Jones', 'Gold', 'Bitcoin'
+    return covid, dow, gold, bitcoin
 
-def wrangle_data(covid, dow, gold, btc):
-    df = pandas.concat([covid, dow, gold, btc], axis=1)
+def wrangle_data(covid, dow, gold, bitcoin):
+    df = pandas.concat([covid, dow, gold, bitcoin], axis=1)
     df = df.loc['2020-02-23':].iloc[:-2]
     df = df.interpolate()
     df.iloc[:, 1:] = df.rolling(10, min_periods=1, center=True).mean().iloc[:, 1:]