From 0359cbab5e672c2f3c06f5e85c01e38fd3b2e93d Mon Sep 17 00:00:00 2001 From: m0n61an Date: Mon, 30 Jun 2014 01:13:34 +0400 Subject: [PATCH 1/2] Update README.rst Correction of spelling error: to_datefame -> to_dateframe --- README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.rst b/README.rst index 5f2a3fa..4216604 100644 --- a/README.rst +++ b/README.rst @@ -163,7 +163,7 @@ as shown in the example below :: This will give you access to the following QuerySet methods: - - ``to_datafame`` + - ``to_dataframe`` - ``to_timeseries`` - ``to_pivot_table`` From f5e7fcb80c83eb2dd64a289a1d64ddc4586d5bc1 Mon Sep 17 00:00:00 2001 From: m0n61an Date: Mon, 30 Jun 2014 13:29:38 +0400 Subject: [PATCH 2/2] Update README.rst Remove duplicate "specified" & correct sample data headers --- README.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.rst b/README.rst index 4216604..6a13886 100644 --- a/README.rst +++ b/README.rst @@ -199,7 +199,7 @@ Create a dataframe using all the fields in your model as follows :: df = qs.to_dataframe() This will include your primary key. To create a DataFrame using specified -specified field names:: +field names:: df = qs.to_dataframe(fieldnames=['age', 'department', 'wage']) @@ -267,7 +267,7 @@ Using a *long* storage format :: Some sample data::: ======== ===== ===== - date mame value + date_ix series_name value ======== ===== ====== 2010-01-01 gdp 204699 @@ -301,7 +301,7 @@ Create a timeseries dataframe :: storage='long') df.head() - date gdp inflation wages + date_ix gdp inflation wages 2010-01-01 204966 2.0 100.7