def test_st_legacy_dataframe(self):
"""Test st._legacy_dataframe."""
df = pd.DataFrame({"one": [1, 2], "two": [11, 22]})
st._legacy_dataframe(df)
el = self.get_delta_from_queue().new_element
self.assertEqual(el.data_frame.data.cols[0].int64s.data, [1, 2])
self.assertEqual(el.data_frame.columns.plain_index.data.strings.data,
["one", "two"])
def _get_unnamed_data_methods(self):
"""DeltaGenerator methods that do not produce named datasets."""
return [
lambda df: st._legacy_dataframe(df),
lambda df: st._legacy_table(df),
lambda df: st._legacy_vega_lite_chart(
df, {"mark": "line", "encoding": {"x": "a", "y": "b"}}
),
# TODO: _legacy_line_chart, _legacy_bar_chart, etc.
]
# Copyright 2018-2021 Streamlit Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import streamlit as st
import pandas as pd
df = pd.DataFrame({"test": [3.14, 3.1]})
st._legacy_dataframe(df.style.format({"test": "{:.2f}"}))
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import streamlit as st
import pandas as pd
import numpy as np
st.header("Empty list")
st.write([])
st.header("Empty dataframes")
st.write(np.array(0))
st.write(pd.DataFrame([]))
st._legacy_dataframe()
st._legacy_dataframe([])
st._legacy_dataframe(np.array(0))
st._legacy_dataframe(pd.DataFrame([]))
st.header("Empty one-column dataframes")
st.write(np.array([]))
st._legacy_dataframe(np.array([]))
st.header("Empty two-column dataframes (only shows 1)")
st.write(pd.DataFrame({"lat": [], "lon": []}))
st._legacy_dataframe(pd.DataFrame({"lat": [], "lon": []}))
st.header("Empty tables")
st._legacy_table()
st._legacy_table([])
# Copyright 2018-2021 Streamlit Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import streamlit as st
import pandas as pd
import numpy as np
def highlight_first(value):
color = "yellow" if value == 0 else "white"
return "background-color: %s" % color
grid = np.arange(0, 100, 1).reshape(10, 10)
df = pd.DataFrame(grid)
st._legacy_dataframe(df.style.applymap(highlight_first))
# Copyright 2018-2022 Streamlit Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import streamlit as st
import pandas as pd
df = pd.DataFrame({"str": ["2020-04-14 00:00:00"]})
df["notz"] = pd.to_datetime(df["str"])
df["yaytz"] = pd.to_datetime(df["str"]).dt.tz_localize("Europe/Moscow")
st._legacy_dataframe(df)
# Copyright 2018-2021 Streamlit Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import streamlit as st import numpy as np import pandas as pd # Explicitly seed the RNG for deterministic results np.random.seed(0) data = np.random.randn(100, 100) df = pd.DataFrame(data) st._legacy_dataframe(df) st._legacy_dataframe(df, 250, 150) st._legacy_dataframe(df, width=250) st._legacy_dataframe(df, height=150)
# * 4 vega-lite charts
# * And then all of the above once again.
# (Also, all of the above should have 3 rows or datapoints.)
# * Then all of the above but with no data.
# * Then a 1-row dataframe and an error
num_rows = 3
df = pd.DataFrame({"a": [1, 2, 3], "b": [10, 0, 30], "c": [100, 200, -100]})
df1 = df.iloc[0:1, :]
for test_type in ["coalesce in Py", "coalesce in JS", "clear after addrows"]:
table_el = st._legacy_table(df1)
dataframe_el = st._legacy_dataframe(df1)
chart_el1 = st._legacy_line_chart()
chart_el2 = st._legacy_line_chart(df1)
# 4 identical charts, built in different ways.
vega_el1 = st._legacy_vega_lite_chart(
df1,
{
"mark": {
"type": "line",
"point": True
},
"encoding": {
"x": {
"field": "a",
"type": "quantitative"
np.random.seed(0)
st.title("Tables with different sizes")
st.header("Long cells that overflow")
st.write("""
Long text should show an ellipsis. All cells should have a tooltip
with their entire un-ellipsized contents.
""")
st._legacy_dataframe({
"foo": ["hello", "world", "foo " * 30],
"bar": ["hello", "world", "bar" * 30],
"baz": [1, 2, 3],
"boz": [1, 2, 3],
"buz": [1, 2, 3],
"biz" * 30: [1, 2, 3],
"bim": [1, 2, 3],
})
st._legacy_dataframe({"foo": ["hello", "world", "foo " * 30]})
ROWS = 2
st.header("Using st._legacy_dataframe")
for cols in [4, 5, 6, 20]:
df = pd.DataFrame(np.random.randn(ROWS, cols),
index=range(ROWS),
columns=range(cols))
